1 code implementation • 10 Feb 2024 • Isaac Corley, Caleb Robinson, Anthony Ortiz
In recent years, there has been an explosion of proposed change detection deep learning architectures in the remote sensing literature.
1 code implementation • 12 Jan 2024 • Caleb Robinson, Isaac Corley, Anthony Ortiz, Rahul Dodhia, Juan M. Lavista Ferres, Peyman Najafirad
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
1 code implementation • 22 May 2023 • Isaac Corley, Caleb Robinson, Rahul Dodhia, Juan M. Lavista Ferres, Peyman Najafirad
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.
no code implementations • 26 Apr 2023 • Isaac Corley, Jonathan Lwowski, Peyman Najafirad
A crucial part of any home is the roof over our heads to protect us from the elements.
no code implementations • 26 Apr 2023 • Isaac Corley, Peyman Najafirad
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.
1 code implementation • 26 Feb 2022 • Isaac Corley, Peyman Najafirad
Remote sensing change detection, identifying changes between scenes of the same location, is an active area of research with a broad range of applications.
1 code implementation • 20 Dec 2019 • Isaac Corley, Jonathan Lwowski, Justin Hoffman
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.
1 code implementation • 14 Nov 2019 • Isaac Corley, Jonathan Lwowski, Justin Hoffman
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.