Blender addons to make the bridge between Blender and geographic data
- blender
- addon
- geospatial
- gis
70,234 visible repositories in the index
1–24 of 39 · Most stars
Blender addons to make the bridge between Blender and geographic data
A command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images. 📷
Free and open-source map hosting solution with custom styles for websites and apps, using OpenStreetMap data
Download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.
Python tools for geographic data
User-friendly, commercial-grade software for processing aerial imagery. ✈️ Download it for free!
TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data
A Python package for segmenting geospatial data with the Segment Anything Model (SAM)
A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
Mesa is an open-source Python library for agent-based modeling, ideal for simulating complex systems and exploring emergent behaviors.
A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment
GeoAI: Artificial Intelligence for Geospatial Data
Rasterio reads and writes geospatial raster datasets
An open source library and framework for deep learning on satellite and aerial imagery.
Documentation and samples for ArcGIS API for Python
Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds
Transform geospatial relations into graphs for Graph Neural Networks and spatial network analysis
Cartopy - a cartographic python library with matplotlib support
PySAL: Python Spatial Analysis Library Meta-Package
This library reads and writes ESRI Shapefiles in pure Python.
Give satellite imagery, terrain data, buildings, streetview photos to your LLM
framework for large-scale SAR satellite data processing
The open source software to build cool stuff with locomotion
Crop Yield Prediction with Deep Learning