crop classification using deep learning on satellite images

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.

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

Information and scripts for the CropAndWeed 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

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.

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

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

Public repository of our work in the search for an optimal multi-view crop classifier (considering encoder architectures and fusion strategies)

Public repository of our IGARSS 2023 submission

Source code from 2022 AI CUP Competition on Crop Status Monitoring by Image Recognition.

This Fiboa extension enables validation against the Hierarchical Crop and Agriculture Taxonomy (HCAT), which harmonizes all declared crops across the European Union.

[TGRS21] Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations