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Parichya Sirohi

Parichya Sirohi

Machine learning researcher and software engineer focused on remote sensing

parichya-sirohi
Joined May 2026
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Summary

Contributor to remote sensing datasets and agricultural machine‑learning research: acknowledged as an early contributor to SICKLE, a multi‑sensor satellite imagery dataset focused on paddy cultivation that produced papers and open code for crop type, phenology and yield prediction tasks. google+2
Published researcher on satellite imagery for agriculture: co‑author on multiple arXiv papers and conference work (2022–2024) addressing crop type identification, high‑resolution imagery applications, and dataset benchmarks. arxiv+3
Experience spanning research and applied roles: holds a B.Tech (Delhi College of Engineering / DTU) and has worked in research assistant and software/data roles across academic and policy organizations (IIIT‑Delhi research work, CEEW listings), reflecting a mix of academic research and applied engineering experience. ac+2
Maintains public developer and social profiles: has a GitHub account and an Instagram profile linked to their name, indicating engagement with open source and personal social presence. github+1

Work

Education

Projects

Writing

SICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated With Multiple Key Cropping Parameters

January 1, 2024

Conference paper (WACV 2024) describing the SICKLE dataset: multi-sensor, multi-resolution satellite imagery annotated with crop type, variety, phenology dates and yield for paddy cultivation; includes dataset benchmarks and usage details.

Favicon imageopenaccess.thecvf.com

Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery

January 1, 2022

Paper analyzing how spatial, temporal, and spectral resolution of satellite imagery affect crop type identification performance for smallholder farms.

Favicon imagearxiv.org

High-Resolution Satellite Imagery for Modeling the Impact of Aridification on Crop Production

January 1, 2022

Paper proposing use of high-resolution satellite imagery and a time-series dataset (SICKLE) to model impacts of aridification on crop production and to predict crop phenology and yield.

Favicon imagearxiv.org