198 citations · 378 across the 15 of their papers we have counts for
15 papers
Global Building Area Estimation Products: How Accurate Are They?
Saad Lahrichi, Doa'a Allabadi, Kyle Bradbury +1
Geo-spatial rasters of building footprint area are useful for a variety of tasks, such as monitoring urbanization, improving energy efficiency, and tracking greenhouse gas emission…
Closing Gaps in Emissions Monitoring with Climate TRACE
Brittany V. Lancellotti, Jordan M. Malof, Aaron Davitt +32
Global greenhouse gas emissions estimates are essential for monitoring and mitigation planning. Existing emissions datasets provide critical foundations for understanding emissions…
Improving and Evaluating Open Deep Research Agents
Doaa Allabadi, Kyle Bradbury, Jordan M. Malof
We focus here on Deep Research Agents (DRAs), which are systems that can take a natural language prompt from a user, and then autonomously search for, and utilize, internet-based c…
Segment anything, from space?
Simiao Ren, Francesco Luzi, Saad Lahrichi +4
Recently, the first foundation model developed specifically for image segmentation tasks was developed, termed the "Segment Anything Model" (SAM). SAM can segment objects in input…
Transformers For Recognition In Overhead Imagery: A Reality Check
Francesco Luzi, Aneesh Gupta, Leslie Collins +2
There is evidence that transformers offer state-of-the-art recognition performance on tasks involving overhead imagery (e.g., satellite imagery). However, it is difficult to make u…
Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis
Simiao Ren, Wei Hu, Kyle Bradbury +4
High quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Unfortunately, actionable information about energy systems is…
Utilizing geospatial data for assessing energy security: Mapping small solar home systems using unmanned aerial vehicles and deep learning
Simiao Ren, Jordan Malof, T. Robert Fetter +3
Solar home systems (SHS), a cost-effective solution for rural communities far from the grid in developing countries, are small solar panels and associated equipment that provides p…
SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems
Yang Xu, Bohao Huang, Xiong Luo +2
Recently deep neural networks (DNNs) have achieved tremendous success for object detection in overhead (e.g., satellite) imagery. One ongoing challenge however is the acquisition o…
Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery
Can Yaras, Kaleb Kassaw, Bohao Huang +2
Modern deep neural networks (DNNs) are highly accurate on many recognition tasks for overhead (e.g., satellite) imagery. However, visual domain shifts (e.g., statistical changes du…
GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery
Bohao Huang, Jichen Yang, Artem Streltsov +3
Energy system information valuable for electricity access planning such as the locations and connectivity of electricity transmission and distribution towers, termed the power grid…
The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation
Fanjie Kong, Bohao Huang, Kyle Bradbury +1
Recently deep learning - namely convolutional neural networks (CNNs) - have yielded impressive performance for the task of building segmentation on large overhead (e.g., satellite)…
What you get is not always what you see: pitfalls in solar array assessment using overhead imagery
Wei Hu, Kyle Bradbury, Jordan M. Malof +5
Effective integration planning for small, distributed solar photovoltaic (PV) arrays into electric power grids requires access to high quality data: the location and power capacity…
Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations
Bohao Huang, Daniel Reichman, Leslie M. Collins +2
In this work we consider the application of convolutional neural networks (CNNs) for pixel-wise labeling (a.k.a., semantic segmentation) of remote sensing imagery (e.g., aerial col…
Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery
Joseph Camilo, Rui Wang, Leslie M. Collins +2
We consider the problem of automatically detecting small-scale solar photovoltaic arrays for behind-the-meter energy resource assessment in high resolution aerial imagery. Such alg…
Automatic Detection of Solar Photovoltaic Arrays in High Resolution Aerial Imagery
Jordan M. Malof, Kyle Bradbury, Leslie M. Collins +1
The quantity of small scale solar photovoltaic (PV) arrays in the United States has grown rapidly in recent years. As a result, there is substantial interest in high quality inform…