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Found 8 papers, 6 papers with code
A Change Detection Reality Check
In recent years, there has been an explosion of proposed change detection deep learning architectures in the remote sensing literature.
Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery
In this work we propose a road segmentation benchmark dataset, Chesapeake Roads Spatial Context (RSC), for evaluating the spatial long-range context understanding of geospatial machine learning models and show how commonly used semantic segmentation models can fail at this task.
Ranked #1 on
Road Segmentation
on ChesapeakeRSC
Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters
Research in self-supervised learning (SSL) with natural images has progressed rapidly in recent years and is now increasingly being applied to and benchmarked with datasets containing remotely sensed imagery.
ZRG: A Dataset for Multimodal 3D Residential Rooftop Understanding
A crucial part of any home is the roof over our heads to protect us from the elements.
Single-View Height Estimation with Conditional Diffusion Probabilistic Models
Digital Surface Models (DSM) offer a wealth of height information for understanding the Earth's surface as well as monitoring the existence or change in natural and man-made structures.
Supervising Remote Sensing Change Detection Models with 3D Surface Semantics
Remote sensing change detection, identifying changes between scenes of the same location, is an active area of research with a broad range of applications.
Destruction of Image Steganography using Generative Adversarial Networks
Digital image steganalysis, or the detection of image steganography, has been studied in depth for years and is driven by Advanced Persistent Threat (APT) groups', such as APT37 Reaper, utilization of steganographic techniques to transmit additional malware to perform further post-exploitation activity on a compromised host.
DomainGAN: Generating Adversarial Examples to Attack Domain Generation Algorithm Classifiers
Our results conclude that GAN based DGAs are superior in evading DGA classifiers in comparison to traditional DGAs, and of the variants, the Wasserstein GAN with Gradient Penalty (WGANGP) is the highest performing DGA for uses both offensively and defensively.
