Depanshu Sani, Sandeep Mahato, Sourabh Saini, Harsh Kumar Agarwal, Charu Chandra Devshali,
Saket Anand, Gaurav Arora, Thiagarajan Jayaraman
(Accepted at WACV 2024 as an Oral presentation)
The availability of well-curated datasets has driven the success of Machine Learning (ML) models. Despite greater access to earth observation data in agriculture, there is a scarcity of curated and labelled datasets, which limits the potential of its use in training ML models for remote sensing (RS) in agriculture. To this end, we introduce a first-of-its-kind dataset called SICKLE, which constitutes a time-series of multi-resolution imagery from 3 distinct satellites: Landsat-8, Sentinel-1 and Sentinel-2. Our dataset constitutes multi-spectral, thermal and microwave sensors during the January 2018 − March 2021 period. We construct each temporal sequence by considering the cropping practices followed by farmers primarily engaged in paddy cultivation in the Cauvery Delta region of Tamil Nadu, India; and annotate the corresponding imagery with key cropping parameters at multiple resolutions (i.e., 3m, 10m and 30m). Our dataset comprises 2,370 season-wise samples from 388 unique plots, having an average size of 0.38 acres, for classifying 21 crop types across 4 districts in the Delta, which amounts to approximately 209,000 satellite images. Out of the 2,370 samples, 351 paddy samples from 145 plots are annotated with multiple crop parameters; such as the variety of paddy, its growing season and productivity in terms of per-acre yields. Ours is also one among the first studies that consider the growing season activities pertinent to crop phenology (spans sowing, transplanting and harvesting dates) as parameters of interest. We benchmark SICKLE on three tasks: crop type, crop phenology (sowing, transplanting, harvesting), and yield prediction.
Depanshu Sani was supported by Google’s AI for Social Good “Impact Scholars” program, 2021. Saket Anand gratefully acknowledges the Infosys Center for Artificial Intelligence at IIIT-Delhi for their partial support. We also appreciate Parichya Sirohi’s contributions in the early stages of the project. Additionally, we wish to express our gratitude to Dr. Gopinath R. and Dr. Rajakumar R. from Ecotechnology, MS Swaminathan Research Foundation, Chennai, for their valuable inputs concerning the study area and assistance with field data collection.
@InProceedings{Sani_2024_WACV,
author = {Sani, Depanshu and Mahato, Sandeep and Saini, Sourabh and Agarwal, Harsh Kumar and Devshali, Charu Chandra and Anand, Saket and Arora, Gaurav and Jayaraman, Thiagarajan},
title = {SICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated With Multiple Key Cropping Parameters},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2024},
pages = {5995-6004}
}
For any queries, reach out to us at depanshus@iiitd.ac.in