Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Robinson, Caleb"'
Autor:
Tadesse, Girmaw Abebe, Robinson, Caleb, Hacheme, Gilles Quentin, Zaytar, Akram, Dodhia, Rahul, Shawa, Tsering Wangyal, Ferres, Juan M. Lavista, Kreike, Emmanuel H.
This study explores object detection in historical aerial photographs of Namibia to identify long-term environmental changes. Specifically, we aim to identify key objects -- Waterholes, Omuti homesteads, and Big trees -- around Oshikango in Namibia u
Externí odkaz:
http://arxiv.org/abs/2404.08544
Autor:
Zaytar, Akram, Robinson, Caleb, Hacheme, Gilles Q., Tadesse, Girmaw A., Dodhia, Rahul, Ferres, Juan M. Lavista, Hughey, Lacey F., Stabach, Jared A., Amoke, Irene
Rare object detection is a fundamental task in applied geospatial machine learning, however is often challenging due to large amounts of high-resolution satellite or aerial imagery and few or no labeled positive samples to start with. This paper addr
Externí odkaz:
http://arxiv.org/abs/2403.02736
In recent years, there has been an explosion of proposed change detection deep learning architectures in the remote sensing literature. These approaches claim to offer state-of-the-art performance on different standard benchmark datasets. However, ha
Externí odkaz:
http://arxiv.org/abs/2402.06994
Satellite data has the potential to inspire a seismic shift for machine learning -- one in which we rethink existing practices designed for traditional data modalities. As machine learning for satellite data (SatML) gains traction for its real-world
Externí odkaz:
http://arxiv.org/abs/2402.01444
Autor:
Robinson, Caleb, Corley, Isaac, Ortiz, Anthony, Dodhia, Rahul, Ferres, Juan M. Lavista, Najafirad, Peyman
Fully understanding a complex high-resolution satellite or aerial imagery scene often requires spatial reasoning over a broad relevant context. The human object recognition system is able to understand object in a scene over a long-range relevant con
Externí odkaz:
http://arxiv.org/abs/2401.06762
Autor:
Roman, Anthony Cintron, Vaughan, Jennifer Wortman, See, Valerie, Ballard, Steph, Torres, Jehu, Robinson, Caleb, Ferres, Juan M. Lavista
This paper introduces a no-code, machine-readable documentation framework for open datasets, with a focus on responsible AI (RAI) considerations. The framework aims to improve comprehensibility, and usability of open datasets, facilitating easier dis
Externí odkaz:
http://arxiv.org/abs/2312.06153
Geographic information is essential for modeling tasks in fields ranging from ecology to epidemiology. However, extracting relevant location characteristics for a given task can be challenging, often requiring expensive data fusion or distillation fr
Externí odkaz:
http://arxiv.org/abs/2311.17179
Autor:
Fobi, Simone, Cardona, Manuel, Collins, Elliott, Robinson, Caleb, Ortiz, Anthony, Sederholm, Tina, Dodhia, Rahul, Ferres, Juan Lavista
This work presents an approach for combining household demographic and living standards survey questions with features derived from satellite imagery to predict the poverty rate of a region. Our approach utilizes visual features obtained from a singl
Externí odkaz:
http://arxiv.org/abs/2307.11921
Autor:
Robinson, Caleb, Nsutezo, Simone Fobi, Ortiz, Anthony, Sederholm, Tina, Dodhia, Rahul, Birge, Cameron, Richards, Kasie, Pitcher, Kris, Duarte, Paulo, Ferres, Juan M. Lavista
Rapid and accurate building damage assessments from high-resolution satellite imagery following a natural disaster is essential to inform and optimize first responder efforts. However, performing such building damage assessments in an automated manne
Externí odkaz:
http://arxiv.org/abs/2306.12589
Autor:
Stewart, Adam J., Lehmann, Nils, Corley, Isaac A., Wang, Yi, Chang, Yi-Chia, Braham, Nassim Ait Ali, Sehgal, Shradha, Robinson, Caleb, Banerjee, Arindam
The Landsat program is the longest-running Earth observation program in history, with 50+ years of data acquisition by 8 satellites. The multispectral imagery captured by sensors onboard these satellites is critical for a wide range of scientific fie
Externí odkaz:
http://arxiv.org/abs/2306.09424