Stars
Official repository for PTAViT3D and PTAViT3DCA models for field boundaries detection using S2 and/or S1 imagery.
Code and data for Kuro Siwo flood mapping dataset
Remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and spectral indices in a sensor-agnostic way.
Remote sensing pretrained models easy loading using huggingface -- PyTorch
Implementation of four novel attention-based CNN classifiers based on EfficientNet B3 backbone.
Super-resolution of 10 Sentinel-2 bands to 5-meter resolution, starting from L1C or L2A (Theia format) products.
CloudS2Mask is a Sentinel 2 L1C cloud and cloud shadow masking library
Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pre…
proof of concept for a transformer-based time series prediction model
Raster-based Spatial Analytics for Python
Sentinel 2 Super Resolution - Enhance the spatial resolution of the 20 m and 60 m bands to the target resolution of 10 m.
EGMS toolkit is a set of python scripts to download and manage the InSAR data from European Ground Motion Service.
GPU accelerated earth observation data processors
GPU-accelerated InSAR processing on Sentinel-1 TOPS data
PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series
PLAnT-ISCE3: Polarimetric Interferometric Lab and Analysis Tool (PLAnT) scripts for the InSAR Scientific Computing Environment 3 (ISCE3)
openmeteopy is a client Python wrapper library for Open-Meteo web API. It allows quick and easy consumption of OM data from Python applications via a simple object model and in a human-friendly fas…
Keras/Pytorch implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.
A Python implementation of the FAO-56 dual crop coefficient approach for crop water use estimation and irrigation scheduling
Source code for the paper "Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture".
Rapid and EAsy Change detection in radar TIme-series by Variation coefficient
Google Research
Implementation of MetNet-3, SOTA neural weather model out of Google Deepmind, in Pytorch