198 citations · 378 across the 15 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…