Search Results for author:
Found 12 papers, 5 papers with code
Analyzing Decades-Long Environmental Changes in Namibia Using Archival Aerial Photography and Deep Learning
This study explores object detection in historical aerial photographs of Namibia to identify long-term environmental changes.
Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery
Rare object detection is a fundamental task in applied geospatial machine learning, however is often challenging due to large amounts of high-resolution satellite or aerial imagery and few or no labeled positive samples to start with.
Weak Labeling for Cropland Mapping in Africa
We conduct experiments to demonstrate the benefits of the improved weak labels generated by our method.
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
Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments
This paper introduces a no-code, machine-readable documentation framework for open datasets, with a focus on responsible AI (RAI) considerations.
Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event
Rapid and accurate building damage assessments from high-resolution satellite imagery following a natural disaster is essential to inform and optimize first responder efforts.
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.
Fast building segmentation from satellite imagery and few local labels
Innovations in computer vision algorithms for satellite image analysis can enable us to explore global challenges such as urbanization and land use change at the planetary level.
TorchGeo: Deep Learning With Geospatial Data
Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neural networks in similar computer vision tasks and the sheer volume of remotely sensed imagery available.
Detecting Cattle and Elk in the Wild from Space
Localizing and counting large ungulates -- hoofed mammals like cows and elk -- in very high-resolution satellite imagery is an important task for supporting ecological studies.
Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery
For instance, in rural settings, the pre-construction area may look similar to the surrounding environment until the building is constructed.
Improving Lesion Detection by exploring bias on Skin Lesion dataset
Bissoto et al. experimented with different bounding-box based masks and showed that deep learning models could classify skin lesion images without clinically meaningful information in the input data.
