Export Training Data for Deep
Learning Tool
أداة تصدير بيانات التدريب للتعلم
العميق
ArcMap
ArcGIS
How to use Export Training
Data for Deep Learning Tool in Arc Toolbox??
كيفية استخدام أداة تصدير بيانات التدريب للتعلم
العميق ؟؟
Path to access the toolمسار الوصول الى الأداة
:
Export Training Data for Deep
Learning Tool, Segmentation and Classification Toolset, Spatial
Analyst Tools Toolbox
Export Training Data for Deep
Learning
Uses a remote sensing
image to convert labeled vector or raster data into deep learning training
datasets. The output is a folder of image chips and a folder of metadata files
in the specified format.
يستخدم صورة الاستشعار عن بعد لتحويل البيانات
المتجهية أو النقطية إلى مجموعات بيانات تدريب التعلم العميق. الإخراج عبارة عن مجلد
من شرائح الصور ومجلد لملفات البيانات الوصفية بالتنسيق المحدد.
1.
Input Raster أدخل البيانات النقطية
The input source
imagery, typically multispectral imagery.
Examples of the type of
input source imagery include multispectral satellite, drone, aerial, or National
Agriculture Imagery Program (NAIP).
صور مصدر الإدخال ، عادةً صور متعددة الأطياف.
تتضمن الأمثلة على نوع صور مصدر الإدخال القمر
الصناعي متعدد الأطياف أو الطائرة بدون طيار أو الجوي أو برنامج الصور الزراعية
الوطني (NAIP).
Output Folder
The folder where the output image chips and metadata will be stored.
Input Feature Class Or Classified
Raster (optional)
The training sample data, in either vector or raster form.
Vector inputs should follow a training sample format as generated by the
ArcGIS Desktop Image Classification toolbar. Raster inputs should follow a
classified raster format as generated by the Classify Raster tool.
Class Value Field (optional)
The field that contains the class values. If no field is specified, the
system searches for a value or classvalue field. If the feature does not
contain a class field, the system determines that all records belong to one
class.
Buffer Radius (optional)
The radius for a buffer around each training sample to delineate a
training sample area. This allows you to create circular polygon training
samples from points.
The linear unit of the Input Feature Class Or Classified Raster spatial
reference is used.
Image Chip Format (optional)
Specifies the raster format for the image chip outputs.
PNG and JPEG support up to 3 bands.
· TIFF—TIFF format
· PNG—PNG format
· JPEG—JPEG format
· MRF—MRF (Meta Raster Format)
Tile Size X (optional)
The size of the image chips for the X dimension.
Tile Size Y (optional)
The size of the image chips for the Y dimension.
Stride X (optional)
The distance to move in the X direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Stride Y (optional)
The distance to move in the Y direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Output NoFeature Tiles (optional)
Specifies whether image chips that do not capture training samples will be
exported.
· Checked—All image chips, including those that do not capture training
samples, will be exported.
· Unchecked—Only image chips that capture training samples will be exported.
This is the default.
If checked, image chips that do not capture labeled data will also be
exported; if not checked, they will not be exported.
Meta Data Format (optional)
Specifies the format of the output metadata labels.
The four options for output metadata labels for the training data are
KITTI rectangles, PASCAL VOC rectangles, Classified Tiles (a class map), and
RCNN Masks. If your input training sample data is a feature class layer, such
as a building layer or standard classification training sample file, use the
KITTI or PASCAL VOC rectangles option. The output metadata is a .txt file or
.xml file containing the training sample data contained in the minimum bounding
rectangle. The name of the metadata file matches the input source image name.
If your input training sample data is a class map, use the Classified Tiles
option as your output metadata format.
· KITTI_rectangles—The metadata follows the same format as the Karlsruhe
Institute of Technology and Toyota Technological Institute (KITTI) Object
Detection Evaluation dataset. The KITTI dataset is a vision benchmark suite.
This is the default.The label files are plain text files. All values, both
numerical and strings, are separated by spaces, and each row corresponds to one
object.
· PASCAL_VOC_rectangles—The metadata follows the same format as the Pattern
Analysis, Statistical Modeling and Computational Learning, Visual Object
Classes (PASCAL_VOC) dataset. The PASCAL VOC dataset is a standardized image
dataset for object class recognition.The label files are XML files and contain
information about image name, class value, and bounding boxes.
· Classified_Tiles—The output will be one classified image chip per input
image chip. No other metadata for each image chip is used. Only the statistics
output has more information on the classes, such as class names, class values,
and output statistics.
· RCNN_Masks—The output will be image chips that have a mask on the areas
where the sample exists. The model generates bounding boxes and segmentation masks
for each instance of an object in the image. It's based on Feature Pyramid
Network (FPN) and a ResNet101 backbone in the deep learning framework model.
For the KITTI metadata format, 15 columns are created, but only 5 of them
are used in the tool. The first column is the class value. The next 3 columns
are skipped. Columns 5-8 define the minimum bounding rectangle, which is
comprised of 4 image coordinate locations: left, top, right, and bottom pixels,
respectively. The minimum bounding rectangle encompasses the training chip used
in the deep learning classifier. The remaining columns are not used.
Start Index (optional)
The start index for the sequence of image chips. This allows you to append
more image chips to an existing sequence. The default value is 0.
2.
Output Folder مجلد الإخراج
The folder where the
output image chips and metadata will be stored.
المجلد حيث سيتم تخزين شرائح الصور الناتجة
والبيانات الوصفية.
Input Feature Class Or Classified
Raster (optional)
The training sample data, in either vector or raster form.
Vector inputs should follow a training sample format as generated by the
ArcGIS Desktop Image Classification toolbar. Raster inputs should follow a
classified raster format as generated by the Classify Raster tool.
Class Value Field (optional)
The field that contains the class values. If no field is specified, the
system searches for a value or classvalue field. If the feature does not
contain a class field, the system determines that all records belong to one
class.
Buffer Radius (optional)
The radius for a buffer around each training sample to delineate a
training sample area. This allows you to create circular polygon training
samples from points.
The linear unit of the Input Feature Class Or Classified Raster spatial reference
is used.
Image Chip Format (optional)
Specifies the raster format for the image chip outputs.
PNG and JPEG support up to 3 bands.
· TIFF—TIFF format
· PNG—PNG format
· JPEG—JPEG format
· MRF—MRF (Meta Raster Format)
Tile Size X (optional)
The size of the image chips for the X dimension.
Tile Size Y (optional)
The size of the image chips for the Y dimension.
Stride X (optional)
The distance to move in the X direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Stride Y (optional)
The distance to move in the Y direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Output NoFeature Tiles (optional)
Specifies whether image chips that do not capture training samples will be
exported.
· Checked—All image chips, including those that do not capture training
samples, will be exported.
· Unchecked—Only image chips that capture training samples will be exported.
This is the default.
If checked, image chips that do not capture labeled data will also be
exported; if not checked, they will not be exported.
Meta Data Format (optional)
Specifies the format of the output metadata labels.
The four options for output metadata labels for the training data are
KITTI rectangles, PASCAL VOC rectangles, Classified Tiles (a class map), and
RCNN Masks. If your input training sample data is a feature class layer, such
as a building layer or standard classification training sample file, use the
KITTI or PASCAL VOC rectangles option. The output metadata is a .txt file or
.xml file containing the training sample data contained in the minimum bounding
rectangle. The name of the metadata file matches the input source image name.
If your input training sample data is a class map, use the Classified Tiles
option as your output metadata format.
· KITTI_rectangles—The metadata follows the same format as the Karlsruhe
Institute of Technology and Toyota Technological Institute (KITTI) Object
Detection Evaluation dataset. The KITTI dataset is a vision benchmark suite.
This is the default.The label files are plain text files. All values, both
numerical and strings, are separated by spaces, and each row corresponds to one
object.
· PASCAL_VOC_rectangles—The metadata follows the same format as the Pattern
Analysis, Statistical Modeling and Computational Learning, Visual Object
Classes (PASCAL_VOC) dataset. The PASCAL VOC dataset is a standardized image
dataset for object class recognition.The label files are XML files and contain
information about image name, class value, and bounding boxes.
· Classified_Tiles—The output will be one classified image chip per input
image chip. No other metadata for each image chip is used. Only the statistics
output has more information on the classes, such as class names, class values,
and output statistics.
· RCNN_Masks—The output will be image chips that have a mask on the areas
where the sample exists. The model generates bounding boxes and segmentation
masks for each instance of an object in the image. It's based on Feature
Pyramid Network (FPN) and a ResNet101 backbone in the deep learning framework
model.
For the KITTI metadata format, 15 columns are created, but only 5 of them
are used in the tool. The first column is the class value. The next 3 columns
are skipped. Columns 5-8 define the minimum bounding rectangle, which is
comprised of 4 image coordinate locations: left, top, right, and bottom pixels,
respectively. The minimum bounding rectangle encompasses the training chip used
in the deep learning classifier. The remaining columns are not used.
Start Index (optional)
The start index for the sequence of image chips. This allows you to append
more image chips to an existing sequence. The default value is 0.
3.
Input Feature Class Or Classified Raster (optional) فئة ميزة الإدخال أو النقطية المصنفة (اختياري)
The training sample
data, in either vector or raster form.
Vector inputs should
follow a training sample format as generated by the ArcGIS Desktop Image
Classification toolbar. Raster inputs should follow a classified raster format
as generated by the Classify Raster tool.
بيانات عينة التدريب ، إما في شكل متجه أو نقطي.
يجب أن تتبع مدخلات المتجه تنسيق عينة تدريب كما
تم إنشاؤه بواسطة شريط أدوات ArcGIS Desktop Image Classification. يجب أن تتبع مدخلات البيانات النقطية تنسيقًا نقطيًا مصنفًا كما
تم إنشاؤه بواسطة أداة Classify
Raster.
Class Value Field (optional)
The field that contains the class values. If no field is specified, the
system searches for a value or classvalue field. If the feature does not
contain a class field, the system determines that all records belong to one
class.
Buffer Radius (optional)
The radius for a buffer around each training sample to delineate a
training sample area. This allows you to create circular polygon training
samples from points.
The linear unit of the Input Feature Class Or Classified Raster spatial
reference is used.
Image Chip Format (optional)
Specifies the raster format for the image chip outputs.
PNG and JPEG support up to 3 bands.
· TIFF—TIFF format
· PNG—PNG format
· JPEG—JPEG format
· MRF—MRF (Meta Raster Format)
Tile Size X (optional)
The size of the image chips for the X dimension.
Tile Size Y (optional)
The size of the image chips for the Y dimension.
Stride X (optional)
The distance to move in the X direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Stride Y (optional)
The distance to move in the Y direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Output NoFeature Tiles (optional)
Specifies whether image chips that do not capture training samples will be
exported.
· Checked—All image chips, including those that do not capture training
samples, will be exported.
· Unchecked—Only image chips that capture training samples will be exported.
This is the default.
If checked, image chips that do not capture labeled data will also be
exported; if not checked, they will not be exported.
Meta Data Format (optional)
Specifies the format of the output metadata labels.
The four options for output metadata labels for the training data are
KITTI rectangles, PASCAL VOC rectangles, Classified Tiles (a class map), and
RCNN Masks. If your input training sample data is a feature class layer, such
as a building layer or standard classification training sample file, use the
KITTI or PASCAL VOC rectangles option. The output metadata is a .txt file or
.xml file containing the training sample data contained in the minimum bounding
rectangle. The name of the metadata file matches the input source image name.
If your input training sample data is a class map, use the Classified Tiles
option as your output metadata format.
· KITTI_rectangles—The metadata follows the same format as the Karlsruhe
Institute of Technology and Toyota Technological Institute (KITTI) Object
Detection Evaluation dataset. The KITTI dataset is a vision benchmark suite.
This is the default.The label files are plain text files. All values, both
numerical and strings, are separated by spaces, and each row corresponds to one
object.
· PASCAL_VOC_rectangles—The metadata follows the same format as the Pattern
Analysis, Statistical Modeling and Computational Learning, Visual Object
Classes (PASCAL_VOC) dataset. The PASCAL VOC dataset is a standardized image
dataset for object class recognition.The label files are XML files and contain
information about image name, class value, and bounding boxes.
· Classified_Tiles—The output will be one classified image chip per input
image chip. No other metadata for each image chip is used. Only the statistics
output has more information on the classes, such as class names, class values, and
output statistics.
· RCNN_Masks—The output will be image chips that have a mask on the areas
where the sample exists. The model generates bounding boxes and segmentation
masks for each instance of an object in the image. It's based on Feature
Pyramid Network (FPN) and a ResNet101 backbone in the deep learning framework
model.
For the KITTI metadata format, 15 columns are created, but only 5 of them
are used in the tool. The first column is the class value. The next 3 columns
are skipped. Columns 5-8 define the minimum bounding rectangle, which is
comprised of 4 image coordinate locations: left, top, right, and bottom pixels,
respectively. The minimum bounding rectangle encompasses the training chip used
in the deep learning classifier. The remaining columns are not used.
Start Index (optional)
The start index for the sequence of image chips. This allows you to append
more image chips to an existing sequence. The default value is 0.
4.
Class Value Field (optional) حقل
قيمة الفئة (اختياري)
The field that contains
the class values. If no field is specified, the system searches for a value or
classvalue field. If the feature does not contain a class field, the system
determines that all records belong to one class.
الحقل الذي يحتوي على قيم الفئة. إذا لم يتم تحديد
أي حقل ، يبحث النظام عن قيمة أو حقل فئة قيمة. إذا كانت الميزة لا تحتوي على حقل
فئة ، يحدد النظام أن جميع السجلات تنتمي إلى فئة واحدة.
Buffer Radius (optional)
The radius for a buffer around each training sample to delineate a
training sample area. This allows you to create circular polygon training
samples from points.
The linear unit of the Input Feature Class Or Classified Raster spatial
reference is used.
Image Chip Format (optional)
Specifies the raster format for the image chip outputs.
PNG and JPEG support up to 3 bands.
· TIFF—TIFF format
· PNG—PNG format
· JPEG—JPEG format
· MRF—MRF (Meta Raster Format)
Tile Size X (optional)
The size of the image chips for the X dimension.
Tile Size Y (optional)
The size of the image chips for the Y dimension.
Stride X (optional)
The distance to move in the X direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Stride Y (optional)
The distance to move in the Y direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Output NoFeature Tiles (optional)
Specifies whether image chips that do not capture training samples will be
exported.
· Checked—All image chips, including those that do not capture training
samples, will be exported.
· Unchecked—Only image chips that capture training samples will be exported.
This is the default.
If checked, image chips that do not capture labeled data will also be
exported; if not checked, they will not be exported.
Meta Data Format (optional)
Specifies the format of the output metadata labels.
The four options for output metadata labels for the training data are
KITTI rectangles, PASCAL VOC rectangles, Classified Tiles (a class map), and
RCNN Masks. If your input training sample data is a feature class layer, such
as a building layer or standard classification training sample file, use the
KITTI or PASCAL VOC rectangles option. The output metadata is a .txt file or
.xml file containing the training sample data contained in the minimum bounding
rectangle. The name of the metadata file matches the input source image name.
If your input training sample data is a class map, use the Classified Tiles
option as your output metadata format.
· KITTI_rectangles—The metadata follows the same format as the Karlsruhe
Institute of Technology and Toyota Technological Institute (KITTI) Object
Detection Evaluation dataset. The KITTI dataset is a vision benchmark suite.
This is the default.The label files are plain text files. All values, both
numerical and strings, are separated by spaces, and each row corresponds to one
object.
· PASCAL_VOC_rectangles—The metadata follows the same format as the Pattern
Analysis, Statistical Modeling and Computational Learning, Visual Object
Classes (PASCAL_VOC) dataset. The PASCAL VOC dataset is a standardized image
dataset for object class recognition.The label files are XML files and contain
information about image name, class value, and bounding boxes.
· Classified_Tiles—The output will be one classified image chip per input
image chip. No other metadata for each image chip is used. Only the statistics
output has more information on the classes, such as class names, class values,
and output statistics.
· RCNN_Masks—The output will be image chips that have a mask on the areas
where the sample exists. The model generates bounding boxes and segmentation
masks for each instance of an object in the image. It's based on Feature
Pyramid Network (FPN) and a ResNet101 backbone in the deep learning framework
model.
For the KITTI metadata format, 15 columns are created, but only 5 of them
are used in the tool. The first column is the class value. The next 3 columns
are skipped. Columns 5-8 define the minimum bounding rectangle, which is
comprised of 4 image coordinate locations: left, top, right, and bottom pixels,
respectively. The minimum bounding rectangle encompasses the training chip used
in the deep learning classifier. The remaining columns are not used.
Start Index (optional)
The start index for the sequence of image chips. This allows you to append
more image chips to an existing sequence. The default value is 0.
5.
Buffer Radius (optional) نصف قطر الحرم
المكاني (اختياري)
The radius for a buffer
around each training sample to delineate a training sample area. This allows
you to create circular polygon training samples from points.
The linear unit of the
Input Feature Class Or Classified Raster spatial reference is used.
نصف قطر الحرم المكاني حول كل عينة تدريب لتحديد
منطقة عينة التدريب. يتيح لك ذلك إنشاء عينات تدريب على شكل مضلع دائري من النقاط.
يتم استخدام الوحدة الخطية لفئة ميزات الإدخال أو
الإسناد المكاني النقطي المصنف.
Image Chip Format (optional)
Specifies the raster format for the image chip outputs.
PNG and JPEG support up to 3 bands.
· TIFF—TIFF format
· PNG—PNG format
· JPEG—JPEG format
· MRF—MRF (Meta Raster Format)
Tile Size X (optional)
The size of the image chips for the X dimension.
Tile Size Y (optional)
The size of the image chips for the Y dimension.
Stride X (optional)
The distance to move in the X direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Stride Y (optional)
The distance to move in the Y direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Output NoFeature Tiles (optional)
Specifies whether image chips that do not capture training samples will be
exported.
· Checked—All image chips, including those that do not capture training
samples, will be exported.
· Unchecked—Only image chips that capture training samples will be exported.
This is the default.
If checked, image chips that do not capture labeled data will also be
exported; if not checked, they will not be exported.
Meta Data Format (optional)
Specifies the format of the output metadata labels.
The four options for output metadata labels for the training data are
KITTI rectangles, PASCAL VOC rectangles, Classified Tiles (a class map), and
RCNN Masks. If your input training sample data is a feature class layer, such
as a building layer or standard classification training sample file, use the
KITTI or PASCAL VOC rectangles option. The output metadata is a .txt file or
.xml file containing the training sample data contained in the minimum bounding
rectangle. The name of the metadata file matches the input source image name.
If your input training sample data is a class map, use the Classified Tiles option
as your output metadata format.
· KITTI_rectangles—The metadata follows the same format as the Karlsruhe
Institute of Technology and Toyota Technological Institute (KITTI) Object
Detection Evaluation dataset. The KITTI dataset is a vision benchmark suite.
This is the default.The label files are plain text files. All values, both
numerical and strings, are separated by spaces, and each row corresponds to one
object.
· PASCAL_VOC_rectangles—The metadata follows the same format as the Pattern
Analysis, Statistical Modeling and Computational Learning, Visual Object
Classes (PASCAL_VOC) dataset. The PASCAL VOC dataset is a standardized image
dataset for object class recognition.The label files are XML files and contain
information about image name, class value, and bounding boxes.
· Classified_Tiles—The output will be one classified image chip per input
image chip. No other metadata for each image chip is used. Only the statistics
output has more information on the classes, such as class names, class values,
and output statistics.
· RCNN_Masks—The output will be image chips that have a mask on the areas
where the sample exists. The model generates bounding boxes and segmentation
masks for each instance of an object in the image. It's based on Feature
Pyramid Network (FPN) and a ResNet101 backbone in the deep learning framework
model.
For the KITTI metadata format, 15 columns are created, but only 5 of them
are used in the tool. The first column is the class value. The next 3 columns
are skipped. Columns 5-8 define the minimum bounding rectangle, which is
comprised of 4 image coordinate locations: left, top, right, and bottom pixels,
respectively. The minimum bounding rectangle encompasses the training chip used
in the deep learning classifier. The remaining columns are not used.
Start Index (optional)
The start index for the sequence of image chips. This allows you to append
more image chips to an existing sequence. The default value is 0.
6.
Image Chip Format (optional) تنسيق
رقاقة الصورة (اختياري)
Specifies the raster
format for the image chip outputs.
PNG and JPEG support up
to 3 bands.
·
TIFF—TIFF format
·
PNG—PNG format
·
JPEG—JPEG format
·
MRF—MRF (Meta Raster Format)
يحدد التنسيق النقطي لمخرجات شريحة الصورة.
يدعم تنسيق PNG و JPEG ما
يصل إلى 3 نطاقات.
• TIFF -
تنسيق TIFF
• PNG -
تنسيق PNG
• JPEG -
تنسيق JPEG
• MRF - MRF (تنسيق Meta
Raster)
Tile Size X (optional)
The size of the image chips for the X dimension.
Tile Size Y (optional)
The size of the image chips for the Y dimension.
Stride X (optional)
The distance to move in the X direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Stride Y (optional)
The distance to move in the Y direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Output NoFeature Tiles (optional)
Specifies whether image chips that do not capture training samples will be
exported.
· Checked—All image chips, including those that do not capture training
samples, will be exported.
· Unchecked—Only image chips that capture training samples will be exported.
This is the default.
If checked, image chips that do not capture labeled data will also be
exported; if not checked, they will not be exported.
Meta Data Format (optional)
Specifies the format of the output metadata labels.
The four options for output metadata labels for the training data are
KITTI rectangles, PASCAL VOC rectangles, Classified Tiles (a class map), and
RCNN Masks. If your input training sample data is a feature class layer, such
as a building layer or standard classification training sample file, use the
KITTI or PASCAL VOC rectangles option. The output metadata is a .txt file or
.xml file containing the training sample data contained in the minimum bounding
rectangle. The name of the metadata file matches the input source image name.
If your input training sample data is a class map, use the Classified Tiles
option as your output metadata format.
· KITTI_rectangles—The metadata follows the same format as the Karlsruhe
Institute of Technology and Toyota Technological Institute (KITTI) Object
Detection Evaluation dataset. The KITTI dataset is a vision benchmark suite.
This is the default.The label files are plain text files. All values, both
numerical and strings, are separated by spaces, and each row corresponds to one
object.
· PASCAL_VOC_rectangles—The metadata follows the same format as the Pattern
Analysis, Statistical Modeling and Computational Learning, Visual Object
Classes (PASCAL_VOC) dataset. The PASCAL VOC dataset is a standardized image
dataset for object class recognition.The label files are XML files and contain
information about image name, class value, and bounding boxes.
· Classified_Tiles—The output will be one classified image chip per input
image chip. No other metadata for each image chip is used. Only the statistics
output has more information on the classes, such as class names, class values,
and output statistics.
· RCNN_Masks—The output will be image chips that have a mask on the areas
where the sample exists. The model generates bounding boxes and segmentation
masks for each instance of an object in the image. It's based on Feature
Pyramid Network (FPN) and a ResNet101 backbone in the deep learning framework
model.
For the KITTI metadata format, 15 columns are created, but only 5 of them
are used in the tool. The first column is the class value. The next 3 columns
are skipped. Columns 5-8 define the minimum bounding rectangle, which is
comprised of 4 image coordinate locations: left, top, right, and bottom pixels,
respectively. The minimum bounding rectangle encompasses the training chip used
in the deep learning classifier. The remaining columns are not used.
Start Index (optional)
The start index for the sequence of image chips. This allows you to append
more image chips to an existing sequence. The default value is 0.
7.
Tile Size X (optional) حجم س
(اختياري)
The size of the image
chips for the X dimension.
حجم شرائح الصورة للبعد X.
Tile Size Y (optional)
The size of the image chips for the Y dimension.
Stride X (optional)
The distance to move in the X direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Stride Y (optional)
The distance to move in the Y direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Output NoFeature Tiles (optional)
Specifies whether image chips that do not capture training samples will be
exported.
· Checked—All image chips, including those that do not capture training
samples, will be exported.
· Unchecked—Only image chips that capture training samples will be exported.
This is the default.
If checked, image chips that do not capture labeled data will also be
exported; if not checked, they will not be exported.
Meta Data Format (optional)
Specifies the format of the output metadata labels.
The four options for output metadata labels for the training data are
KITTI rectangles, PASCAL VOC rectangles, Classified Tiles (a class map), and
RCNN Masks. If your input training sample data is a feature class layer, such
as a building layer or standard classification training sample file, use the
KITTI or PASCAL VOC rectangles option. The output metadata is a .txt file or
.xml file containing the training sample data contained in the minimum bounding
rectangle. The name of the metadata file matches the input source image name.
If your input training sample data is a class map, use the Classified Tiles
option as your output metadata format.
· KITTI_rectangles—The metadata follows the same format as the Karlsruhe
Institute of Technology and Toyota Technological Institute (KITTI) Object
Detection Evaluation dataset. The KITTI dataset is a vision benchmark suite.
This is the default.The label files are plain text files. All values, both
numerical and strings, are separated by spaces, and each row corresponds to one
object.
· PASCAL_VOC_rectangles—The metadata follows the same format as the Pattern
Analysis, Statistical Modeling and Computational Learning, Visual Object
Classes (PASCAL_VOC) dataset. The PASCAL VOC dataset is a standardized image
dataset for object class recognition.The label files are XML files and contain
information about image name, class value, and bounding boxes.
· Classified_Tiles—The output will be one classified image chip per input
image chip. No other metadata for each image chip is used. Only the statistics
output has more information on the classes, such as class names, class values,
and output statistics.
· RCNN_Masks—The output will be image chips that have a mask on the areas
where the sample exists. The model generates bounding boxes and segmentation
masks for each instance of an object in the image. It's based on Feature
Pyramid Network (FPN) and a ResNet101 backbone in the deep learning framework
model.
For the KITTI metadata format, 15 columns are created, but only 5 of them
are used in the tool. The first column is the class value. The next 3 columns
are skipped. Columns 5-8 define the minimum bounding rectangle, which is comprised
of 4 image coordinate locations: left, top, right, and bottom pixels,
respectively. The minimum bounding rectangle encompasses the training chip used
in the deep learning classifier. The remaining columns are not used.
Start Index (optional)
The start index for the sequence of image chips. This allows you to append
more image chips to an existing sequence. The default value is 0.
8.
Tile Size Y (optional) حجم ص
(اختياري)
The size of the image
chips for the Y dimension.
حجم شرائح الصورة للبعد Y.
Stride X (optional)
The distance to move in the X direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Stride Y (optional)
The distance to move in the Y direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Output NoFeature Tiles (optional)
Specifies whether image chips that do not capture training samples will be
exported.
· Checked—All image chips, including those that do not capture training
samples, will be exported.
· Unchecked—Only image chips that capture training samples will be exported.
This is the default.
If checked, image chips that do not capture labeled data will also be
exported; if not checked, they will not be exported.
Meta Data Format (optional)
Specifies the format of the output metadata labels.
The four options for output metadata labels for the training data are
KITTI rectangles, PASCAL VOC rectangles, Classified Tiles (a class map), and
RCNN Masks. If your input training sample data is a feature class layer, such
as a building layer or standard classification training sample file, use the
KITTI or PASCAL VOC rectangles option. The output metadata is a .txt file or
.xml file containing the training sample data contained in the minimum bounding
rectangle. The name of the metadata file matches the input source image name.
If your input training sample data is a class map, use the Classified Tiles
option as your output metadata format.
· KITTI_rectangles—The metadata follows the same format as the Karlsruhe
Institute of Technology and Toyota Technological Institute (KITTI) Object
Detection Evaluation dataset. The KITTI dataset is a vision benchmark suite.
This is the default.The label files are plain text files. All values, both
numerical and strings, are separated by spaces, and each row corresponds to one
object.
· PASCAL_VOC_rectangles—The metadata follows the same format as the Pattern
Analysis, Statistical Modeling and Computational Learning, Visual Object
Classes (PASCAL_VOC) dataset. The PASCAL VOC dataset is a standardized image
dataset for object class recognition.The label files are XML files and contain
information about image name, class value, and bounding boxes.
· Classified_Tiles—The output will be one classified image chip per input
image chip. No other metadata for each image chip is used. Only the statistics
output has more information on the classes, such as class names, class values,
and output statistics.
· RCNN_Masks—The output will be image chips that have a mask on the areas
where the sample exists. The model generates bounding boxes and segmentation
masks for each instance of an object in the image. It's based on Feature
Pyramid Network (FPN) and a ResNet101 backbone in the deep learning framework
model.
For the KITTI metadata format, 15 columns are created, but only 5 of them
are used in the tool. The first column is the class value. The next 3 columns
are skipped. Columns 5-8 define the minimum bounding rectangle, which is
comprised of 4 image coordinate locations: left, top, right, and bottom pixels,
respectively. The minimum bounding rectangle encompasses the training chip used
in the deep learning classifier. The remaining columns are not used.
Start Index (optional)
The start index for the sequence of image chips. This allows you to append
more image chips to an existing sequence. The default value is 0.
9.
Stride X (optional) س (اختياري)
The distance to move in
the X direction when creating the next image chips.
When stride is equal to
tile size, there will be no overlap. When stride is equal to half the tile
size, there will be 50 percent overlap.
المسافة للتحرك في الاتجاه X عند
إنشاء شرائح الصورة التالية.
عندما تكون الخطوة مساوية لحجم البلاط ، لن يكون
هناك تداخل. عندما تكون الخطوة مساوية لنصف حجم البلاط ، سيكون هناك تداخل بنسبة
50 بالمائة.
Stride Y (optional)
The distance to move in the Y direction when creating the next image
chips.
When stride is equal to tile size, there will be no overlap. When stride
is equal to half the tile size, there will be 50 percent overlap.
Output NoFeature Tiles (optional)
Specifies whether image chips that do not capture training samples will be
exported.
· Checked—All image chips, including those that do not capture training
samples, will be exported.
· Unchecked—Only image chips that capture training samples will be exported.
This is the default.
If checked, image chips that do not capture labeled data will also be
exported; if not checked, they will not be exported.
Meta Data Format (optional)
Specifies the format of the output metadata labels.
The four options for output metadata labels for the training data are
KITTI rectangles, PASCAL VOC rectangles, Classified Tiles (a class map), and
RCNN Masks. If your input training sample data is a feature class layer, such
as a building layer or standard classification training sample file, use the
KITTI or PASCAL VOC rectangles option. The output metadata is a .txt file or
.xml file containing the training sample data contained in the minimum bounding
rectangle. The name of the metadata file matches the input source image name.
If your input training sample data is a class map, use the Classified Tiles
option as your output metadata format.
· KITTI_rectangles—The metadata follows the same format as the Karlsruhe
Institute of Technology and Toyota Technological Institute (KITTI) Object
Detection Evaluation dataset. The KITTI dataset is a vision benchmark suite.
This is the default.The label files are plain text files. All values, both
numerical and strings, are separated by spaces, and each row corresponds to one
object.
· PASCAL_VOC_rectangles—The metadata follows the same format as the Pattern
Analysis, Statistical Modeling and Computational Learning, Visual Object
Classes (PASCAL_VOC) dataset. The PASCAL VOC dataset is a standardized image
dataset for object class recognition.The label files are XML files and contain
information about image name, class value, and bounding boxes.
· Classified_Tiles—The output will be one classified image chip per input
image chip. No other metadata for each image chip is used. Only the statistics
output has more information on the classes, such as class names, class values,
and output statistics.
· RCNN_Masks—The output will be image chips that have a mask on the areas
where the sample exists. The model generates bounding boxes and segmentation
masks for each instance of an object in the image. It's based on Feature
Pyramid Network (FPN) and a ResNet101 backbone in the deep learning framework
model.
For the KITTI metadata format, 15 columns are created, but only 5 of them
are used in the tool. The first column is the class value. The next 3 columns
are skipped. Columns 5-8 define the minimum bounding rectangle, which is
comprised of 4 image coordinate locations: left, top, right, and bottom pixels,
respectively. The minimum bounding rectangle encompasses the training chip used
in the deep learning classifier. The remaining columns are not used.
Start Index (optional)
The start index for the sequence of image chips. This allows you to append
more image chips to an existing sequence. The default value is 0.
10.
Stride Y (optional) ص (اختياري)
The distance to move in
the Y direction when creating the next image chips.
When stride is equal to
tile size, there will be no overlap. When stride is equal to half the tile
size, there will be 50 percent overlap.
مسافة التحرك في الاتجاه Y عند
إنشاء شرائح الصورة التالية.
عندما تكون الخطوة مساوية لحجم البلاط ، لن يكون
هناك تداخل. عندما تكون الخطوة مساوية لنصف حجم البلاط ، سيكون هناك تداخل بنسبة
50 بالمائة.
Output NoFeature Tiles (optional)
Specifies whether image chips that do not capture training samples will be
exported.
· Checked—All image chips, including those that do not capture training
samples, will be exported.
· Unchecked—Only image chips that capture training samples will be exported.
This is the default.
If checked, image chips that do not capture labeled data will also be exported;
if not checked, they will not be exported.
Meta Data Format (optional)
Specifies the format of the output metadata labels.
The four options for output metadata labels for the training data are
KITTI rectangles, PASCAL VOC rectangles, Classified Tiles (a class map), and
RCNN Masks. If your input training sample data is a feature class layer, such
as a building layer or standard classification training sample file, use the
KITTI or PASCAL VOC rectangles option. The output metadata is a .txt file or
.xml file containing the training sample data contained in the minimum bounding
rectangle. The name of the metadata file matches the input source image name.
If your input training sample data is a class map, use the Classified Tiles
option as your output metadata format.
· KITTI_rectangles—The metadata follows the same format as the Karlsruhe
Institute of Technology and Toyota Technological Institute (KITTI) Object
Detection Evaluation dataset. The KITTI dataset is a vision benchmark suite.
This is the default.The label files are plain text files. All values, both
numerical and strings, are separated by spaces, and each row corresponds to one
object.
· PASCAL_VOC_rectangles—The metadata follows the same format as the Pattern
Analysis, Statistical Modeling and Computational Learning, Visual Object
Classes (PASCAL_VOC) dataset. The PASCAL VOC dataset is a standardized image
dataset for object class recognition.The label files are XML files and contain
information about image name, class value, and bounding boxes.
· Classified_Tiles—The output will be one classified image chip per input
image chip. No other metadata for each image chip is used. Only the statistics
output has more information on the classes, such as class names, class values,
and output statistics.
· RCNN_Masks—The output will be image chips that have a mask on the areas
where the sample exists. The model generates bounding boxes and segmentation
masks for each instance of an object in the image. It's based on Feature
Pyramid Network (FPN) and a ResNet101 backbone in the deep learning framework
model.
For the KITTI metadata format, 15 columns are created, but only 5 of them
are used in the tool. The first column is the class value. The next 3 columns
are skipped. Columns 5-8 define the minimum bounding rectangle, which is
comprised of 4 image coordinate locations: left, top, right, and bottom pixels,
respectively. The minimum bounding rectangle encompasses the training chip used
in the deep learning classifier. The remaining columns are not used.
Start Index (optional)
The start index for the sequence of image chips. This allows you to append
more image chips to an existing sequence. The default value is 0.
11.
Output NoFeature Tiles (optional) المخرج
بدون معالم (اختياري)
Specifies whether image
chips that do not capture training samples will be exported.
·
Checked—All image chips, including those that do not capture
training samples, will be exported.
·
Unchecked—Only image chips that capture training samples will be
exported. This is the default.
If checked, image chips
that do not capture labeled data will also be exported; if not checked, they
will not be exported.
يحدد ما إذا كان سيتم تصدير شرائح الصور التي لا
تلتقط عينات التدريب.
• تم التحديد - سيتم
تصدير جميع شرائح الصور ، بما في ذلك تلك التي لا تلتقط عينات التدريب.
• غير محدد - سيتم تصدير
شرائح الصور التي تلتقط عينات التدريب فقط. هذا هو الافتراضي.
إذا تم تحديده ، فسيتم أيضًا تصدير شرائح الصور
التي لا تلتقط البيانات المصنفة ؛ إذا لم يتم تحديدها ، فلن يتم تصديرها.
Meta Data Format (optional)
Specifies the format of the output metadata labels.
The four options for output metadata labels for the training data are
KITTI rectangles, PASCAL VOC rectangles, Classified Tiles (a class map), and
RCNN Masks. If your input training sample data is a feature class layer, such
as a building layer or standard classification training sample file, use the
KITTI or PASCAL VOC rectangles option. The output metadata is a .txt file or
.xml file containing the training sample data contained in the minimum bounding
rectangle. The name of the metadata file matches the input source image name.
If your input training sample data is a class map, use the Classified Tiles
option as your output metadata format.
· KITTI_rectangles—The metadata follows the same format as the Karlsruhe
Institute of Technology and Toyota Technological Institute (KITTI) Object
Detection Evaluation dataset. The KITTI dataset is a vision benchmark suite.
This is the default.The label files are plain text files. All values, both
numerical and strings, are separated by spaces, and each row corresponds to one
object.
· PASCAL_VOC_rectangles—The metadata follows the same format as the Pattern
Analysis, Statistical Modeling and Computational Learning, Visual Object
Classes (PASCAL_VOC) dataset. The PASCAL VOC dataset is a standardized image
dataset for object class recognition.The label files are XML files and contain
information about image name, class value, and bounding boxes.
· Classified_Tiles—The output will be one classified image chip per input
image chip. No other metadata for each image chip is used. Only the statistics
output has more information on the classes, such as class names, class values,
and output statistics.
· RCNN_Masks—The output will be image chips that have a mask on the areas
where the sample exists. The model generates bounding boxes and segmentation
masks for each instance of an object in the image. It's based on Feature
Pyramid Network (FPN) and a ResNet101 backbone in the deep learning framework
model.
For the KITTI metadata format, 15 columns are created, but only 5 of them
are used in the tool. The first column is the class value. The next 3 columns
are skipped. Columns 5-8 define the minimum bounding rectangle, which is
comprised of 4 image coordinate locations: left, top, right, and bottom pixels,
respectively. The minimum bounding rectangle encompasses the training chip used
in the deep learning classifier. The remaining columns are not used.
Start Index (optional)
The start index for the sequence of image chips. This allows you to append
more image chips to an existing sequence. The default value is 0.
12.
Meta Data Format (optional) تنسيق
البيانات الوصفية (اختياري)
Specifies the format of
the output metadata labels.
The four options for
output metadata labels for the training data are KITTI rectangles, PASCAL VOC
rectangles, Classified Tiles (a class map), and RCNN Masks. If your input
training sample data is a feature class layer, such as a building layer or
standard classification training sample file, use the KITTI or PASCAL VOC
rectangles option. The output metadata is a .txt file or .xml file containing
the training sample data contained in the minimum bounding rectangle. The name
of the metadata file matches the input source image name. If your input
training sample data is a class map, use the Classified Tiles option as your
output metadata format.
·
KITTI_rectangles—The metadata follows the same format as the
Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI)
Object Detection Evaluation dataset. The KITTI dataset is a vision benchmark
suite. This is the default.The label files are plain text files. All values,
both numerical and strings, are separated by spaces, and each row corresponds
to one object.
·
PASCAL_VOC_rectangles—The metadata follows the same format as the
Pattern Analysis, Statistical Modeling and Computational Learning, Visual
Object Classes (PASCAL_VOC) dataset. The PASCAL VOC dataset is a standardized
image dataset for object class recognition.The label files are XML files and
contain information about image name, class value, and bounding boxes.
·
Classified_Tiles—The output will be one classified image chip per
input image chip. No other metadata for each image chip is used. Only the
statistics output has more information on the classes, such as class names,
class values, and output statistics.
·
RCNN_Masks—The output will be image chips that have a mask on the
areas where the sample exists. The model generates bounding boxes and
segmentation masks for each instance of an object in the image. It's based on
Feature Pyramid Network (FPN) and a ResNet101 backbone in the deep learning
framework model.
For the KITTI metadata
format, 15 columns are created, but only 5 of them are used in the tool. The
first column is the class value. The next 3 columns are skipped. Columns 5-8
define the minimum bounding rectangle, which is comprised of 4 image coordinate
locations: left, top, right, and bottom pixels, respectively. The minimum
bounding rectangle encompasses the training chip used in the deep learning
classifier. The remaining columns are not used.
يحدد تنسيق تسميات البيانات الأولية للمخرجات.
الخيارات الأربعة لتسميات البيانات الوصفية
للمخرجات لبيانات التدريب هي مستطيلات KITTI ،
ومستطيلات PASCAL VOC ، والبلاط المصنف (خريطة
الفصل) ، وأقنعة RCNN. إذا كانت بيانات نموذج
تدريب الإدخال عبارة عن طبقة فئة معلم ، مثل طبقة بناء أو ملف عينة تدريب على
التصنيف القياسي ، فاستخدم خيار مستطيلات KITTI أو PASCAL VOC. البيانات الوصفية للمخرجات هي ملف .txt أو
ملف .xml يحتوي على بيانات عينة التدريب الموجودة في الحد الأدنى لمستطيل
الإحاطة. يتطابق اسم ملف البيانات الأولية مع اسم صورة مصدر الإدخال. إذا كانت
بيانات عينة تدريب الإدخال الخاصة بك عبارة عن خريطة فصل ، فاستخدم الخيار Classified Tiles كتنسيق بيانات تعريف الإخراج.
• KITTI_rectangles - تتبع البيانات الوصفية نفس تنسيق مجموعة بيانات تقييم اكتشاف
الكائنات لمعهد كارلسروه للتكنولوجيا ومعهد تويوتا التكنولوجي (KITTI). مجموعة
بيانات KITTI هي مجموعة معايير
الرؤية. هذا هو الإعداد الافتراضي ، ملفات التسمية هي ملفات نصية عادية. يتم فصل
جميع القيم ، العددية والسلاسل ، بمسافات ، ويتوافق كل صف مع كائن واحد.
• PASCAL_VOC_rectangles - تتبع البيانات الوصفية نفس تنسيق مجموعة بيانات تحليل الأنماط
والنمذجة الإحصائية والتعلم الحسابي وفئات الكائنات المرئية (PASCAL_VOC). مجموعة بيانات PASCAL VOC هي
مجموعة بيانات صور قياسية للتعرف على فئة الكائن. ملفات الملصقات هي ملفات XML
وتحتوي على معلومات حول اسم الصورة وقيمة الفئة والمربعات المحيطة.
• Classified_Tiles - سيكون الإخراج عبارة عن شريحة صورة مصنفة واحدة لكل شريحة صورة
إدخال. لا يتم استخدام أي بيانات وصفية أخرى لكل شريحة صورة. يحتوي إخراج
الإحصائيات فقط على مزيد من المعلومات حول الفئات ، مثل أسماء الفئات وقيم الفئة
وإحصائيات الإخراج.
• RCNN_Masks - سيكون الناتج عبارة عن شرائح صور بها قناع على المناطق التي
توجد بها العينة. يقوم النموذج بإنشاء مربعات محيط وأقنعة تجزئة لكل مثيل لكائن في
الصورة. يعتمد على شبكة هرم الميزات (FPN)
والعمود الفقري ResNet101 في نموذج إطار عمل
التعلم العميق.
بالنسبة إلى تنسيق البيانات الوصفية لـ KITTI ، يتم
إنشاء 15 عمودًا ، ولكن يتم استخدام 5 منهم فقط في الأداة. العمود الأول هو قيمة
الفئة. تم تخطي الأعمدة الثلاثة التالية. تحدد الأعمدة 5-8 الحد الأدنى لمستطيل
الإحاطة ، والذي يتكون من 4 مواقع إحداثيات للصورة: وحدات بكسل يسار ، وأعلى ،
ويمين ، وأسفل ، على التوالي. يشمل الحد الأدنى لمستطيل الإحاطة شريحة التدريب
المستخدمة في مصنف التعلم العميق. لم يتم استخدام الأعمدة المتبقية.
Start Index (optional)
The start index for the sequence of image chips. This allows you to append
more image chips to an existing sequence. The default value is 0.
13.
Start Index (optional) فهرس البداية
(اختياري)
The start index for the
sequence of image chips. This allows you to append more image chips to an
existing sequence. The default value is 0.
فهرس البداية لتسلسل شرائح الصور. يتيح لك ذلك
إلحاق المزيد من شرائح الصور بالتسلسل الحالي. القيمة الافتراضية هي 0.
اليك صفحه ومجموعة على الفيس بوك لتعلم أكثر بما يخص نظم المعلومات الجغرافية (GIS) و برنامج ArcGIS Pro من خلال هذه الروابط:
مجموعة على الفيس بوك
ArcGIS Pro من
هنا.
مجموعة على الفيس بوك
GIS for WE - ArcGIS Pro من
هنا.صفحة الفيس بوك
GIS for WE من
هنا.
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