Deep Learning with Imagery

Applications of imagery deep learning models in ArcGIS.

Object Classification

Models: FeatureClassifier with ResNet, Inception, VGG backbones

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Classifying homes as damaged or not after a forest fire

Object Detection

Models: SingleShotDetector(SSD), RetinaNet, YOLO, FasterRCNN, MMDetection

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Detecting swimming pools

Pixel Classification

Models: UNetClassifier, PSPNetClassifier, DeepLab, MMSegmentation

Land cover classification

Instance Segmentation

Models: MaskRCNN

Extracting building footprints

Panoptic Segmentation

Models: MaxDeepLab

Land cover Extraction, Building Footprint and Car Detection

Edge Detection

Models: BDCNEdgeDetector, HEDEdgeDetector, ConnectNet

Mapping residential parcels

Road Extraction

Models: MultiTaskRoadExtractor

Road extraction

Change Detection

Models: STA-Net ChangeDetector

Identifying new construction

Image to Image Translation

Models: Pix2Pix, Pix2PixHD, CycleGAN

Generating optical imagery from SAR data

Image Captioning

Model: ImageCaptioner

Describing geographical features using a caption

Image Enhancement

Models: SuperResolution

Increasing image resolution

Imagery Time Series Classification

Models: PSETAE

Crop Classification