Deep learning model architectures

Available with Image Analyst license.

The following table provides an overview of the deep learning model types available in ArcGIS AllSource. Each row provides compatible metadata formats and the main use of the specific model type. Where available, accompanying examples are included.

Deep learning model typeSupported metadataTaskExample

BDCN Edge Detector

Classified tiles

Pixel classification

Land parcel extraction

Change Detector

Classified tiles

Pixel classification (change detection)

Detect building change

ConnectNet

Classified tiles

Pixel classification

CycleGAN

Export tiles

CycleGAN

Image translation (unpaired images)

SAR to RGB translation

DeepLab

Classified tiles

Pixel classification

Deep Sort

Imagenet

Object Tracker

DETReg

PASCAL_VOC_rectangles

Object detection

Faster RCNN

PASCAL_VOC_rectangles

KITTI_rectangles

Object detection

Feature classifier

Labeled tiles

Imagenet

Multilabeled tiles

Object detection

Feature categorization

HED Edge Detector

Classified tiles

Pixel classification

Land parcel extraction

Image Captioner

Image captioning

Image captioning

Mask RCNN

RCNN masks

Object detection (instance segmentation)

Caribou detection and classification

MMDetection

PASCAL_VOC_rectangles

KITTI_rectangles

Object detection

MMSegmentation

Classified tiles

Pixel classification

Multi-Task Road Extractor

Classified tiles

Pixel classification

Automatic road extraction

MaX-DeepLab

Panoptic segmentation

Panoptic segmentation

Pix2Pix

Export tiles

Image translation (paired images)

Colorizing historic imagery

PSPNet

Classified tiles

Pixel classification

PSETAE

MaskRCNN

Pixel classification

RetinaNet

PASCAL_VOC_rectangles

KITTI_rectangles

Object detection

Utility and vegetation detection

SAMLoRA

Classified tiles

Pixel classification

Siam Mask

RCNN Masks

Object tracking

SSD

PASCAL_VOC_rectangles

KITTI_rectangles

Object detection

Detect palm tree health

Super-resolution

Superresolution

Image translation (paired images)

Increase image resolution

U-Net

Classified tiles

Pixel classification

Extracting building footprints

YOLOv3

PASCAL_VOC_rectangles

KITTI_rectangles

Object detection

Note:

Some of the examples that use the Python notebook for training can be performed using the Train Deep Learning Model tool.

Deep Learning tasks and tools

TaskTool

Change detection

Detect Change Using Deep Learning

Image translation (paired and unpaired)

Classify Pixels Using Deep Learning

Object classification

Classify Objects Using Deep Learning

Object detection

Detect Objects Using Deep Learning

Object detection (instance segmentation)

Detect Objects Using Deep Learning

Object tracking

FMV Tracking tab

Pixel classification

Classify Pixels Using Deep Learning

Related topics