🛰️ Geospatial Data Scientist | 🌾 AI for Agriculture | 🌍 Remote Sensing & Earth Observation Enthusiast
Krishnagopal Halder is a Research Scientist at the Leibniz Centre for Agricultural Landscape Research (ZALF) in Müncheberg, Germany. He is part of the “Multi-Scale Modelling and Forecasting” working group under the Leibniz-Lab Systemic Sustainability (LL SYSTAIN) project, focusing on the intersections of biodiversity, climate, agriculture, and food systems. His research integrates geospatial data science, remote sensing, and machine learning to develop robust and scalable models for agricultural monitoring and environmental assessment.
Krishnagopal’s work leverages cloud computing platforms such as Google Earth Engine, as well as open-source ML/DL frameworks including PyTorch and Scikit-Learn. He has contributed to several international projects such as SynPAI and AgML, which aim to bridge process-based and data-driven modeling approaches for crop yield prediction and sustainability analysis. He is also a published researcher in Q1-ranked journals on topics like flood mapping, landslide prediction, and groundwater assessment.
- 🌱 LL SYSTAIN – Systemic Sustainability: Biodiversity, Climate, and Agriculture Intersections
- 🧠 SynPAI – Synergizing Process-based and ML Models for Crop Yield Prediction
- 🌾 AgML – Machine Learning for Agricultural Modeling
🧭 Also proficient in: Google Earth Engine, ArcGIS, QGIS, Scikit-learn, Remote Sensing Analysis, Machine Learning, Deep Learning, Feature Engineering
- 🥇 GATE 2024 (Geomatics Engineering) – All India Rank 36
- 🏅 Ranked in Top 0.01% in WB Higher Secondary Board (479/500)
- 📚 4x First-author / co-author in Q1-ranked peer-reviewed journals
(Scientific Reports, Environmental Sciences Europe, GNH Risk)
- 🌍 Halder et al., (2025): Improving Landslide Susceptibility via Meta-Learning. Scientific Reports 🔗
- 🚜 Halder et al., (2024): SAR-driven flood inventory using ML. GNH Risk 🔗
- 💧 Halder et al., (2024): Groundwater Mapping in Eastern India. Env. Sci. Europe 🔗