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Found 12 papers, 4 papers with code
Segment anything, from space?
In this work, we examine whether SAM's performance extends to overhead imagery problems and help guide the community's response to its development.
Transformers For Recognition In Overhead Imagery: A Reality Check
In this paper we systematically compare the impact of adding transformer structures into state-of-the-art segmentation models for overhead imagery.
Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis
These include the opportunity to extend the methods beyond electricity to broader energy systems and wider geographic areas; and the ability to expand the use of these methods in research and decision making as satellite data become cheaper and easier to access.
Utilizing geospatial data for assessing energy security: Mapping small solar home systems using unmanned aerial vehicles and deep learning
In this work, we explore the viability and cost-performance tradeoff of using automatic SHS detection on unmanned aerial vehicle (UAV) imagery as an alternative to satellite imagery.
SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems
Recently deep neural networks (DNNs) have achieved tremendous success for object detection in overhead (e. g., satellite) imagery.
Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery
Modern deep neural networks (DNNs) are highly accurate on many recognition tasks for overhead (e. g., satellite) imagery.
GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery
Energy system information valuable for electricity access planning such as the locations and connectivity of electricity transmission and distribution towers, termed the power grid, is often incomplete, outdated, or altogether unavailable.
The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation
Recently deep learning - namely convolutional neural networks (CNNs) - have yielded impressive performance for the task of building segmentation on large overhead (e. g., satellite) imagery benchmarks.
What you get is not always what you see: pitfalls in solar array assessment using overhead imagery
Our findings suggest that traditional performance evaluation of the automated identification of solar PV from satellite imagery may be optimistic due to common limitations in the validation process.
Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations
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 color or hyperspectral imagery).
Segmentation Of Remote Sensing Imagery
Semantic Segmentation
Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery
In this work, we employ a state-of-the-art convolutional neural network architecture, called SegNet (Badrinarayanan et.
Automatic Detection of Solar Photovoltaic Arrays in High Resolution Aerial Imagery
Unfortunately, existing methods for obtaining this information, such as surveys and utility interconnection filings, are limited in their completeness and spatial resolution.
