Techniques for deep learning with satellite & aerial imagery
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Updated
May 25, 2024
Techniques for deep learning with satellite & aerial imagery
🛰️ List of satellite image training datasets with annotations for computer vision and deep learning
Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds
interactive notebooks from Planet Engineering
TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data
Download and process satellite imagery in Python using Sentinel Hub services.
Search and download Copernicus Sentinel satellite images
A curated list of awesome tools, tutorials, code, projects, links, stuff about Earth Observation, Geospatial Satellite Imagery
Interactive interface for browsing global, full-resolution satellite imagery
SpatioTemporal Asset Catalog specification - making geospatial assets openly searchable and crawlable
Core OSSIM (Open Source Software Image Map) package including C++ code for OSSIM library, command-line applications, tests, and build system
A ready-to-use curated list of Spectral Indices for Remote Sensing applications.
A curated list of resources focused on Machine Learning in Geospatial Data Science.
Repository for Digital Earth Australia Jupyter Notebooks: tools and workflows for geospatial analysis with Open Data Cube and Xarray
🌱 Deep Learning for Instance Segmentation of Agricultural Fields - Master thesis
Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.
TernausNetV2: Fully Convolutional Network for Instance Segmentation
Data Preparation for Satellite Machine Learning
Helper package with multiple U-Net implementations in Keras as well as useful utility tools helpful when working with image semantic segmentation tasks. This library and underlying tools come from multiple projects I performed working on semantic segmentation tasks
A set of tools to use in Google Earth Engine Code Editor (JavaScript)
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