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20192026
most citedDestruction of Image Steganography using Generative Adversarial Networks

14 citations · 48 across the 33 of their papers we have counts for

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Showing 2026Show all

15 papers · 1 filter

cs.CV2026

Planetary Feature Fields are Scalable Earth Representations

Arjun Rao, Sebastian Loeschcke, Anthony Fuller +3

Satellite observations, precomputed embeddings, and map products describe the same evolving Earth, yet are stored as independent, petabyte-scale data products. Their continued grow…

cs.CV2026

Chronosphere: Space-Time Tessellation of Local Climate Experts

Daniel Cher, Eric Xing, Kexing Li +3

We introduce Chronosphere, a spatio-temporal neural field that learns representations of climate. A central challenge in geographic representation learning is modeling environmenta…

cs.CV2026

Infra-Bench CLS: A Global, Open-Source Benchmark for Critical Infrastructure Classification with Earth Observation Foundation Models

Justin Guthrie, Edward Oughton, Konrad Wessels +2

Critical infrastructure location data is often incomplete and unevenly distributed globally, especially in developing regions. Earth observation foundation models are proposed as a…

cs.CV2026

Beyond Accuracy: Assessing Calibration of Geospatial Foundation Models and Their Sensitivity to Distribution Shifts

Nils Lehmann, Jakob Gawlikowski, Burak Ekim +2

Geospatial Foundation Models (GeoFMs) are most commonly ranked and selected by accuracy on standard benchmark conditions via averaged ranks. We show that this protocol is too narro…

cs.CV2026

HeatCast: A Benchmark for Neighborhood-Scale LST Forecasting across 124 U.S. Cities

Jesus Guerrero, Isaac Corley, Leon Najafirad +2

Land Surface Temperature (LST) is a widely used satellite-derived measure of urban surface heat, but there is no shared benchmark for forecasting it at 30 m. Prior studies usually…

cs.CV2026

Earth Embeddings

Adam J. Stewart, Heng Fang, Isaac A. Corley +1

Earth observation is moving from foundation models that users must run themselves toward embedding products that package model feature outputs as reusable data without needing to d…

cs.CV2026

Fields of the Planet: Field Boundary Mapping Beyond 10m

Isaac Corley, Caleb Robinson, Jennifer Marcus +1

Field-boundary maps support crop monitoring, irrigation planning, and yield estimation, but many smallholder parcels span only a few 10 m Sentinel-2 pixels. We introduce Fields of…

cs.CV2026★ 1 cited

No One Knows the State of the Art in Geospatial Foundation Models

Isaac Corley, Nils Lehmann, Caleb Robinson +6

Geospatial foundation models (GFMs) have been proposed as generalizable backbones for disaster response, land-cover mapping, food-security monitoring, and other high-stakes Earth-o…

cs.CV2026★ 1 cited

The first global agricultural field boundary map at 10m resolution

Caleb Robinson, Gedeon Muhawenayo, Subash Khanal +9

The agricultural field is the natural unit at which crops are planted, managed, regulated, and reported, yet most global remote-sensing products for agriculture are only available…

cs.CV2026

Are Pretrained Image Matchers Good Enough for SAR-Optical Satellite Registration?

Isaac Corley, Alex Stoken, Gabriele Berton

Cross-modal optical--SAR (Synthetic Aperture Radar) registration is a bottleneck in remote-sensing disaster response. Modern image matchers are developed and benchmarked almost exc…

cs.CV2026

PRUE: A Practical Recipe for Field Boundary Segmentation at Scale

Gedeon Muhawenayo, Caleb Robinson, Subash Khanal +10

Large-scale maps of field boundaries are essential for agricultural monitoring tasks. Existing deep learning approaches for satellite-based field mapping are sensitive to illuminat…

cs.SE2026

XMENTOR: A Rank-Aware Aggregation Approach for Human-Centered Explainable AI in Just-in-Time Software Defect Prediction

Saumendu Roy, Banani Roy, Chanchal Roy +1

Machine learning (ML)-based defect prediction models can improve software quality. However, their opaque reasoning creates an HCI challenge because developers struggle to trust mod…

cs.CV2026

Advancing Earth Observation Through Machine Learning: A TorchGeo Tutorial

Caleb Robinson, Nils Lehmann, Adam J. Stewart +4

Earth observation machine learning pipelines differ fundamentally from standard computer vision workflows. Imagery is typically delivered as large, georeferenced scenes, labels may…

cs.SE2026

Earth Embeddings as Products: Taxonomy, Ecosystem, and Standardized Access

Heng Fang, Adam J. Stewart, Isaac Corley +2

Geospatial Foundation Models (GFMs) provide powerful representations, but high compute costs hinder their widespread use. Pre-computed embedding data products offer a practical "fr…

cs.CV2026

Fields of The World: A Field Guide for Extracting Agricultural Field Boundaries

Isaac Corley, Hannah Kerner, Caleb Robinson +1

Field boundary maps are a building block for agricultural data products and support crop monitoring, yield estimation, and disease estimation. This tutorial presents the Fields of…