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Found 6 papers, 3 papers with code
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and connectivity of electricity transmission and distribution towers, termed the power grid, is often incomplete, outdated, or altogether unavailable.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
