Free and open source library for AI object detection and semantic segmentation in geospatial rasters. 🚀

[TPAMI 2025] CrossEarth: Geospatial Vision Foundation Model for Cross-Domain Generalization in Remote Sensing Semantic Segmentation

Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery (CVPR 2020 & TPAMI 2023) https://arxiv.org/pdf/2011.09766.pdf

The official repo of CrossEarth-SAR, a sar-centric and billion-scale geospatial foundation model for cross-domain semantic segmentation

Geospatial Image Segmentation with Meta's Segment Anything Model 3

Official repo for [CVPR 2026] "UniGeoSeg: Towards Unified Open-World Segmentation for Geospatial Scenes"

Quad key object used for Geospatial segmentation

Seg2Map is an interactive web map app for geospatial image segmentation using deep learning

Revolutionize geospatial analysis with Swin-UNet – a cutting-edge solution for satellite imagery segmentation using Swin Transformers and UNet. Achieve SOTA precision in road extraction, ideal for GIS professionals, urban planners, and AI researchers. Train, test, and infer with just three lines of code.

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.

🌊 Deep Sales Analysis of Olist E-Commerce: EDA | Time Series| Viz | RFM | NLP | Geospatial | Segmentation & Actionable Business Recommendations.

Multiclass semantic segmentation on geospatial multispectral data

Fine-tuning Geospatial Foundation Models (Prithvi, TerraMind) for building footprint segmentation from Sentinel-2 using TerraTorch — Algiers case study

Identifying point-of-interests within satellite images

A satellite image segmentation project using DeepLabV3+ for land cover classification. The project integrates Google Earth Engine (GEE) for data collection, applies advanced image augmentation, and uses TensorFlow to train a semantic segmentation model. Ideal for geospatial analysis and remote sensing applications.

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

Python-based LiDAR point cloud semantic segmentation pipeline using deep learning. Includes data preprocessing, model training, evaluation, and 3D visualization on indoor and outdoor point clouds. Designed for scalable geospatial and autonomous sensing workflows.

IIT Ropar Internship Project: A geospatial analysis pipeline for Land Use Land Cover (LULC) classification of the Singrauli mining region. Integrates Google Earth Engine (GEE) for Sentinel-2 processing with Optuna-tuned ML and Deep Learning (CNNs) for 6-class segmentation.

- Developed interactive Tableau dashboards to analyze fictional Superstore sales data, focusing on sales performance, regional trends, and customer segmentation to drive strategic business decisions. - Utilized geospatial and trend analysis to uncover seasonal patterns, market penetration, and sales fluctuations, providing actionable insights.

LAM (Locate Anything Model) — standalone prompt based image segmentation + geospatial tiling as a service.

The sKwanda_V2: OpenGeoAI-Dataset is a high-resolution optical imagery dataset, designed to support advanced research in geospatial machine learning applications, such as semantic segmentation, land cover classification, and spatial analysis.

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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

Command line interface for geospatial image segmentation

An integrated framework for automated pavement crack detection, segmentation, and geospatial mapping

Use the HLS Geospatial model made by NASA and IBM to generate masks for use in training a fine-tuned segmentation model.

Segmentation and analysis of data across both space and time dimensions — identifying patterns and clusters in geospatial time-series data using machine learning techniques.

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.