A Python package for segmenting geospatial data with the Segment Anything Model (SAM)

A QGIS plugin tool using Segment Anything Model (SAM) to accelerate segmenting or delineating landforms in geospatial raster images.

Geospatial Image Segmentation with Meta's Segment Anything Model 3

Running Segment Anything Geospatial over Earth Engine exports with Dataflow at scale.

R 'reticulate' wrapper for `segment-geospatial` 'Python' package for segmenting geospatial data with the ‘Segment Anything Model’ (SAM).

A repository for segmenting satellite images using VM-UNet, a variant of the Mamba architecture originally designed for medical image segmentation. This project adapts VM-UNet for geospatial applications, optimizing it for multi-channel satellite imagery.

Three workflows based on aerial imagery or LiDAR point clouds to evaluate the surface available on rooftops by detecting objects.

Identifying point-of-interests within satellite images

An example of how to use Python's geospatial tools to clip a shapefile of roads into segments using a polygon layer.

Auto-segmenting and labelling satellite images using Python and transformer models (SAM, Grounding DINO) on Umbra SAR data

Segment Anything Model for Geospatia lotemachi example for Japanese.

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Test analysis on satellite images for semantic segmentation

A deep-learning project for performing semantic segmentation on geospatial imagery, using architectures such as U-Net, LinkNet and DeepLab v3+ to extract meaningful features from overhead or satellite image datasets.

Building segmentation model using YOLO and geospatial data

Benchmark performance of Nvidia GPUs on a Segment Anything Geospatial task.

This repository contains code for generating Sentinel-2 imagery using GEE and SentinelHub Python API and applying Segment Anything Geospatial at scale.

Advanced geospatial workflow integrating ArcGIS, aerial imagery, and the Segment Anything Model (SAM) for tree segmentation and urban greening analysis. Designed for GIS education, GeoAI research, and reproducible spatial intelligence workflows.

This documentation provides a step-by-step guide to building a low-latency pipeline segment for processing credit card transaction data. The pipeline applies time-series analysis (e.g., velocity checks) and geospatial enrichment to enable rules-based fraud flagging.

A comprehensive geospatial framework for assessing coastal vulnerability and ecosystem service distribution along the Greek coastline. The framework integrates two complementary components: a Coastal Erosion Vulnerability Index applied to all identified beach units, and Coastal Flood Risk Indexes on low-lying and urbanized coastal segments

SAR-DeepSeg is an end-to-end pipeline for semantic segmentation of Synthetic Aperture Radar (SAR) images. It employs deep learning models to process and segment SAR imagery, facilitating accurate interpretation for applications such as land cover segmentation, environmental monitoring, and disaster response, enhancing geospatial analysis.

A conda-smithy repository for segment-geospatial.

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Notebook for segmenting image using Segment-Anything

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End-to-end geospatial semantic segmentation pipeline that fetches satellite imagery from a user-defined region, performs scalable tiling-based inference, and reconstructs large-area segmentation outputs.

Applied K-means clustering to identify regional housing value patterns, showcasing skill in identifying structural patterns

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