Posts by Collection

portfolio

publications

Label Super-Resolution Networks

Kolya Malkin, Caleb Robinson, Le Hou, Rachel Soobitsky, Jacob Czawlytko, Dimitris Samaras, Joel Saltz, Lucas Joppa, Nebojsa Jojic. "Label Super-Resolution Networks." International Conference on Learning Representations (ICLR), 2019.

Human-Machine Collaboration for Fast Land Cover Mapping

Caleb Robinson, Anthony Ortiz, Kolya Malkin, Blake Elias, Andi Peng, Dan Morris, Bistra Dilkina, Nebojsa Jojic. "Human-Machine Collaboration for Fast Land Cover Mapping." AAAI Conference on Artificial Intelligence (AAAI), 2020.

Local Context Normalization: Revisiting Local Normalization

Anthony Ortiz, Caleb Robinson, Dan Morris, Olac Fuentes, Christopher Kiekintveld, Md Hassan, Nebojsa Jojic. "Local Context Normalization: Revisiting Local Normalization." IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

Becoming Good at AI for Good

Meghana Kshirsagar, Caleb Robinson, Siyu Yang, Shahrzad Gholami, Ivan Klyuzhin, Sumit Mukherjee, Md Nasir, Anthony Ortiz, Felipe Oviedo, Darren Tanner, Anusua Trivedi, Yixi Xu, Ming Zhong, Bistra Dilkina, Rahul Dodhia, Juan Ferres. "Becoming Good at AI for Good." 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2021.

Torchgeo: Deep Learning with Geospatial Data

Adam Stewart, Caleb Robinson, Isaac Corley, Anthony Ortiz, Juan Ferres, Arindam Banerjee. "Torchgeo: Deep Learning with Geospatial Data." 30th International Conference on Advances in Geographic Information Systems (SIGSPATIAL), 2022.

A Biologist's Guide to the Galaxy: Leveraging Artificial Intelligence and Very High-Resolution Satellite Imagery to Monitor Marine Mammals from Space

Christin Khan, Kimberly Goetz, Hannah Cubaynes, Caleb Robinson, Erin Murnane, Tyler Aldrich, Meredith Sackett, Penny Clarke, Michelle LaRue, Timothy White, Kathleen Leonard, Anthony Ortiz, Juan Ferres. "A Biologist's Guide to the Galaxy: Leveraging Artificial Intelligence and Very High-Resolution Satellite Imagery to Monitor Marine Mammals from Space." Journal of Marine Science and Engineering, 2023.

Mask Conditional Synthetic Satellite Imagery

Van Le, Varshini Reddy, Zixi Chen, Mengyuan Li, Xinran Tang, Anthony Ortiz, Simone Nsutezo, Caleb Robinson. "Mask Conditional Synthetic Satellite Imagery." arXiv preprint arXiv:2302.04305, 2023.

The Road to India's Renewable Energy Transition Must Pass through Crowded Lands

Joseph Kiesecker, Shivaprakash Nagaraju, James Oakleaf, Anthony Ortiz, Juan Ferres, Caleb Robinson, Srinivas Krishnaswamy, Raman Mehta, Rahul Dodhia, Jeffrey Evans, Michael Heiner, Pratiti Priyadarshini, Pooja Chandran, Kei Sochi. "The Road to India's Renewable Energy Transition Must Pass through Crowded Lands." Land, 2023.

SSL4EO-L: Datasets and Foundation Models for Landsat Imagery

Adam Stewart, Nils Lehmann, Isaac Corley, Yi Wang, Yi-Chia Chang, Nassim Ait, Shradha Sehgal, Caleb Robinson, Arindam Banerjee. "SSL4EO-L: Datasets and Foundation Models for Landsat Imagery." Advances in Neural Information Processing Systems (NeurIPS), 2024.

We introduce SSL4EO-L, the first ever dataset designed for self-supervised learning for Earth Observation for the Landsat family of satellites.

Paper / Code

Seeing the Roads Through the Trees: A Benchmark for Modeling Spatial Dependencies with Aerial Imagery

Caleb Robinson, Isaac Corley, Anthony Ortiz, Rahul Dodhia, Juan Ferres, Peyman Najafirad. "Seeing the Roads Through the Trees: A Benchmark for Modeling Spatial Dependencies with Aerial Imagery." arXiv preprint arXiv:2401.06762, 2024.

We introduce a novel remote sensing dataset for evaluating a model’s ability to learn long-range spatial dependencies in aerial imagery by performing road extraction while containing large gaps occluded by tree canopy.

Paper / Code

Weak Labeling for Cropland Mapping in Africa

Gilles Hacheme, Akram Zaytar, Girmaw Tadesse, Caleb Robinson, Rahul Dodhia, Juan Ferres, Stephen Wood. "Weak Labeling for Cropland Mapping in Africa." arXiv preprint arXiv:2401.07014, 2024.

We propose a simple method for extracting stronger labels from weak cropland labels and an unsupervised segmentation of satellite imagery. We show, in a scenario in Kenya where we only have 33 human-annotated labels, that adding strong labels mined by our method increases the F1 score for the cropland category from 0.53 (without mining) to 0.84.

Paper

Analyzing Decades-Long Environmental Changes in Namibia Using Archival Aerial Photography and Deep Learning

Girmaw Tadesse, Caleb Robinson, Gilles Quantin Hacheme, Akram Zaytar, Rahul Dodhia, Tsering Wangyal Shawa, Juan M Lavista Ferres, Emmanuel H Kreike. "Analyzing Decades-Long Environmental Changes in Namibia Using Archival Aerial Photography and Deep Learning." arXiv preprint arXiv:2404.08544, 2024.

We train and validate semantic segmentation models on historical aerial imagery from 1943 and 1972 for identifing trees, omuti (homesteads), and waterholes. These features are important for understanding how northern Namibia has changed over time. We observe average F1 scores of 0.661 and 0.755 for the 1943 and 1972 imagery respectively. Finally, we run our 1972 model over 5,000 square kilometers to get a first look at the historical population distribution in this area.

Paper

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.