crop-classification

crop classification using deep learning on satellite images

[WACV 2023] Information and scripts for the CropAndWeed Dataset

Sen4AgriNet: A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning

Deep-Plant: Plant Classification with CNN/RNN. It consists of CAFFE/Tensorflow implementation of our PR-17, TIP-18 (HGO-CNN & PlantStructNet) and MalayaKew dataset.

Winning Solutions from Crop Type Detection Competition at CV4A workshop, ICLR 2020

[RSE 2021] Crop mapping from image time series: deep learning with multi-scale label hierarchies

EuroCropsML is a ready-to-use benchmark dataset for few-shot crop type classification using Sentinel-2 imagery.

Code for the paper Multi Modal Deep Learning Based Crop Classification Using Multispectral and Multitemporal Satellite Imagery published in KDD Applied Data Science Track 2020

A dataset with Space (Sentinel-1/2) and Ground (street-level images) components, annotated with crop-type labels for agriculture monitoring.

Public repository of our IGARSS 2023 submission

Crop Classification of Remotely Sensed Images containing Multi Temporal and Multispectral Information

Python data pipeline to acquire, clean, and calculate vegetation indices from Sentinel-2 satellite image. Package is available only for our clients.

Crop classification in the Cauvery Delta Zone using a multichannel based transformer model

Crop classification from Sentinel-2 imagery using Graph Convolutional Networks (PyTorch Geometric) | 99.9%% accuracy | Interactive Streamlit demo

Public repository of our JAG paper

Preprocessing and harmonization scripts for IACS/GSA data.

Comparative study of 5 satellite sensors (Sentinel-1, Sentinel-2, Landsat-8, MODIS, S1+S2 fusion) and 4 ML classifiers (RF, CART, GTB, SVM) for Rabi-season crop classification in Jhansi District, India - built on Google Earth Engine with 1,941 field-verified ground truth points.

Desktop app for land-cover classification from Sentinel-2 time series, with per-class accuracy and domain-shift diagnosis, 3D canopy simulation, and solar/wind resource for the same area. Wails + React + Python; STAC/COG imagery, local-first.