Crop Type Classification

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17 papers5 papers in the last four weeks, up 67% on the four weeks before. 0.0% of all new papers.

Crop Mapping Evaluation under Sampling Bias
Oct 31, 2026Akram Zaytar, Kemunto Ochwang'i, Caleb Robinson +5Crop Type Classification Prediction-Powered Inference
Microsoft AI for Good Research Lab · Microsoft Research
UnMixMamba: Unmixing Guided State Space Subpixel Mapping of Crops from MODIS Time Series
Oct 28, 2026Motasem Alkayid, Yimin Zhu, Naser El-Sheimy +1Crop Type Classification Agricultural Remote Sensing
Department of Geomatics Engineering, University of Calgary, Canada · Department of Geography, Faculty of Arts, The University of Jordan, Amman, Jordan
Ground-to-Satellite Knowledge Distillation for Crop Classification under Temporal and Geographic Shift
Oct 27, 2026Miro Miranda, Rocco Sedona, Valerio Marsocci +2Crop Type Classification Agricultural Remote Sensing
Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany · European Space Agency (ESA/ESRIN), Italy · University of Iceland, Iceland +1
Evaluating AlphaEarth Embeddings for Crop Classification: Separability, Temporal Dynamics, and Cross-Regional Transferability
Oct 27, 2026Priyanka Goyal, Maitreyee Sar, Adway Mitra +1Crop Type Classification Agricultural Image Analysis
Department of Artificial Intelligence, Indian Institute of Technology Kharagpur, West Bengal, India · Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, India · Tata Consultancy Services Research, Kolkata, India
A100: A Hybrid Vision Foundation Model for Agricultural Plant Image Analysis
Oct 16, 2026Mayur Pratim Das, Ajai Dubey, Aksshay Mathew P +5Agricultural Image Analysis Vision Foundation Models
Annam.AI, IIT Ropar Rupnagar, India · IIT Ropar Rupnagar, India · Dept. of DSE, IISER Bhopal Bhopal, India +1
A Dual-Track Curation-and-Classification Framework for Resolving Ground-Truth Label Noise in Operational Sentinel-2 Wheat Area Estimation
Sep 29, 2026Kasimali Agharia, Ujjwal Kumar GuptaAgricultural Image Analysis Agricultural Remote Sensing
Space Applications Centre (SAC), Indian Space Research Organisation (ISRO)
Transferability and operational reliability of a Prithvi crop classification foundation model under phenological and geographic shift across three continents
Sep 20, 2026Venkatesh Kolluru, Rajat Shinde, Abdelhak Marouane +7Crop Type Classification Geospatial Foundation Models
Earth System Science Center, University of Alabama in Huntsville, Huntsville, AL 35899, USA · Space and Earth Science Data Analysis · NASA Marshall Space Flight Center
Configurable Multi-Stage Vision Pipeline for Crop Disease and Pest Diagnosis
Sep 18, 2026Naga Ganesh, Chandrashekar M S, Lakshmi Pedapudi +2Agricultural Image Analysis Plant Disease Classification
Digital Green
AdapterMoE: A Two-Stage Hard-Routing Mixture-of-Experts Architecture for Multi-Crop Disease Recognition with Calibrated Rejection and Incremental Learning
Aug 9, 2026Pin-Hsun Huang, Shaou-Gang MiaouClass-Incremental Learning Plant Disease Classification
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan, Taiwan
Multi-Year Geospatial Reasoning using Interannually-Consistent Historical Predictions as a Free Input Modality
Aug 6, 2026Syed Roshaan Ali Shah, Kasper Bonte, David Bekaert +2Agricultural Image Analysis Agricultural Remote Sensing
VITO Remote Sensing, Mol, Belgium
PhenoStitch: Training-Free Panoptic Crop Mapping from Satellite Image Time Series
Aug 1, 2026Xuechen LiPanoptic Segmentation Agricultural Remote Sensing
University of Minnesota Minneapolis, MN, USA
Task-Conditioned Synthetic Data Generation for Improving Machine Learning Performance in Agricultural Prediction Tasks
Jul 4, 2026Hamid Ebrahimy, Moritz Lucas, Martin AtzmuellerSynthetic Data Augmentation Crop Type Classification
Osnabrück University, Joint Lab Artificial Intelligence and Data Science, Osnabrück, Germany · Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Potsdam, Germany · German Research Center for Artificial Intelligence (DFKI), Research Department Cooperative and Autonomous Systems (CAS), Osnabrück, Germany
Intercomparison of Machine Learning Algorithms for Remote Sensing-based In-season Crop Mapping
Jun 4, 2026August Posch, Jitendra Kumar, Forrest M. Hoffman +1Agricultural Remote Sensing Remote Sensing
Institute for Experiential Artificial Intelligence and Sustainability and Data Sciences Lab, Northeastern University, 216 Massachusetts Avenue, Boston, MA 02155 USA · Environmental Sciences Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37830 USA · Computational Science and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37830 USA
Mapping Tomato Cropping Systems in California Using AlphaEarth Geospatial Embeddings and Deep Learning Analysis
May 20, 2026Mohammadreza Narimani, Alireza Pourreza, Parastoo FarajpoorRemote Sensing Image Segmentation Agricultural Remote Sensing
Department of Biological and Agricultural Engineering, University of California, Davis, Davis, CA, 95616, USA
The role of spatial context and multitask learning in the detection of organic and conventional farming systems based on Sentinel-2 time series
Mar 25, 2026Jan Hemmerling, Marcel Schwieder, Philippe Rufin +4Crop Type Classification Spatial Context Modeling
Thünen Earth Observation (ThEO), Thünen Institute of Farm Economics, Braunschweig, Germany. · Earth Observation Lab, Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany. · Earth and Life Institute, UCLouvain, 1348 Louvain-la-Neuve, Belgium. +2
DirPA: Addressing Prior Shift in Imbalanced Few-shot Crop-type Classification
Mar 13, 2026Joana Reuss, Ekaterina Gikalo, Marco KörnerCrop Type Classification Class-Imbalanced Learning
Technical University of Munich (TUM), TUM School of Engineering and Design, Department of Aerospace and Geodesy, Chair of Remote Sensing Technology, Munich, 80333, Germany · Technical University of Munich (TUM), Munich Data Science Institute (MDSI), Garching, 85748, Germany · ELLIS Unit Jena, University of Jena, Jena, Germany, Jena, 07743, Germany
$T^{3}S$ : Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series
Jun 15, 2025Mehmet Ozgur Turkoglu, Selene Ledain, Jeffrey Zweidler +2Agricultural Image Analysis Agricultural Remote Sensing
Earth Observation of Agroecosystems Team, Agroscope, Switzerland · ETH Zurich, Switzerland

Oct 31, 2026J/K move · Enter open · S save

Crop Mapping Evaluation under Sampling Bias

Akram Zaytar, Kemunto Ochwang'i, Caleb Robinson, Girmaw A. Tadesse+4

Microsoft AI for Good Research Lab · Microsoft Research

Crop Type Classification Prediction-Powered Inference Inverse Probability Weighting

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Government agencies use crop maps to estimate production, monitor food security, and set agricultural policy. These crop maps are usually validated against field labels, but these labels are rarely a random sample of the area the map covers. Field teams tend to label parcels near roads and skip ones that are small or hard to reach, so performance measured on held-out labels mainly describes the places they visited. We call the difference between this reported performance and performance over the whole mapped region the deployment performance gap. To estimate performance over the whole region, we use a weighted form of prediction-powered inference (PPI). The classifier's predictions on unlabeled points across the region give a first estimate, and its errors on the labeled points correct that estimate, with more weight on labels from under-sampled conditions. We test this in five European regions where crop registers label every declared parcel, so the true performance is known. Simulating six documented collection practices there produces gaps of up to 0.20 macro-F1. On the collection rule that creates the largest gap in each region, 5 km block cross-validation still overestimates macro-F1 by as much as 0.18, while PPI with weights estimated from satellite embeddings reduces the largest error across regions to 0.07. Importance-weighted cross-validation with the same weights does almost as well, so most of the improvement comes from the weights themselves. We also apply the method to three Kenyan datasets that have no region-wide reference labels. For a roadside dataset collected with helmet-mounted cameras, the estimated macro-F1 falls from 0.93 under random cross-validation to 0.61, although the correction relies on a small number of heavily weighted labels. Our code is available at https://github.com/microsoft/pappi.