ChesapeakeRSC

Chesapeake Roads Spatial Context

Dataset Information
Modalities
Images
Introduced
2024
License
MIT
Homepage

Overview

A novel remote sensing dataset for evaluating a geospatial machine learning model's ability to learn long range dependencies and spatial context understanding. We create a task to use as a proxy for this by training models to extract roads which have been broken into disjoint pieces due to tree canopy occluding large portions of the road.

The dataset consists of 30,000 RGBN NAIP images and land cover annotations from the Chesapeake Conservacy containing significant amounts of the Tree Canopy Over Road category.

Variants: ChesapeakeRSC

Associated Benchmarks

This dataset is used in 1 benchmark:

Recent Benchmark Submissions

Task Model Paper Date
Road Segmentation U-Net (ResNet-18) Seeing the roads through the … 2024-01-12
Road Segmentation DeepLabV3+ (ResNet-18) Seeing the roads through the … 2024-01-12
Road Segmentation U-Net (ResNet-50) Seeing the roads through the … 2024-01-12
Road Segmentation FCN Seeing the roads through the … 2024-01-12

Research Papers

Recent papers with results on this dataset: