DOI: 10.5281/zenodo.17253084
title: Mid-Atlantic CHM and Digital Elevation Models Dataset

description: This dataset delivers high‑resolution (1‑meter per pixel) Canopy Height Models (CHMs) and Digital Elevation Models (DEMs) for the mid‑Atlantic United States. The products are generated by training Deep‑Learning (DL) models concurrently on high‑resolution multispectral imagery and high-resolution LiDAR samples from NSF’s NEON (National Ecological Observatory Network) project, which provide supervisory signals during training. This fusion yields a cost‑effective, scalable alternative and complement to wall‑to‑wall airborne LiDAR surveys, producing highly accurate canopy structure estimates that improve upon the quality of current DL‑based CHM products. Developed through innovative PhD research at the University of Delaware, the dataset supports applications across ecology, forestry, urban planning, hydrology, and climate science, from biodiversity conservation to disaster preparedness. The data are freely available to accelerate research and inform decision‑making across disciplines.

The dataset is based on multispectral imagery acquired in 2023 and will be updated with 2025 imagery. Additional temporal layers can be produced whenever new high-resolution multispectral data become available, either captured or DL-super-resolved, enabling ongoing monitoring over time.

 

The project website https://zaidud.github.io/midatlantic-elevationmap/ provides interactive maps for visualization of the dataset and easy download of subregions.
description: Impact
This dataset’s high resolution and open access enable transformative applications across multiple fields:







Ecology and Biodiversity: Maps habitats for species like birds and mammals, monitors invasive species, and assesses ecosystem health in mid-Atlantic forests.

Forestry: Estimates above-ground biomass for carbon accounting, monitors forest health, and supports sustainable timber management.

Urban Planning: Guides urban forestry and green space planning in cities, mitigating urban heat islands.

Hydrology: Models flood risks and watershed dynamics in the Mid-Atlantic region, aiding water resource management.

Disaster Management: Assesses wildfire risks and post-storm vegetation damage, enhancing resilience in coastal zones.

Climate Research: Supports carbon sequestration studies and land use change analysis under climate scenarios.





Stakeholders include





University of Delaware (Department of Geography and Spatial Sciences, College of Agriculture & Natural Resources, Plant and Soil Sciences, Data Science Institute, College of Earth, Ocean and Environment, Department of Civil Engineering, among others)

Government agencies (USGS, Delaware DNREC, Chesapeake Bay Program)

Private sectors (environmental consulting, agrotech)

Global researchers.


Open access amplifies collaboration, enabling interdisciplinary research and policy decisions.

 
description: Dataset Information



Spatial Coverage: Mid-Atlantic U.S., including Delaware, Maryland, New Jersey, and Virginia (Bounding Box: 36.5636, -83.7165, 41.4006, -73.8454).

Horizontal resolution: 1 meter per pixel.

Format: GeoTIFF (raster).

Units: Height in meters.

Temporal Scope: Generated from multispectral imagery captured in 2023

Size: Approximately 468 GB (compressed).


 
description: Data Access



Project Website:  https://zaidud.github.io/midatlantic-elevationmap/ provides interactive maps for visualization of the dataset and easy download of subregions.

Dataset Directory: Due to Zenodo's limitations,  the whole dataset is hosted in

https://www.eecis.udel.edu/~arce/mida-elevation/

GitHub: Extra information and usage scripts can be found in the dataset's GitHub repository https://github.com/ZaidUD/midatlantic-elevationmap/blob/main
description: Methodology



The CHM and DEM are generated using a deep generative learning model designed for image translation, pretrained on depth estimation. The model was trained to estimate vegetation height from 1m-resolution multispectral imagery, leveraging image-translation and depth estimation techniques to generate a realistic elevation model.


During training, the model used the High-resolution orthorectified camera imagery mosaic (multispectral) and the Ecosystem structure (CHM) from the NEON project by NSF. NEON's multispectral imagery and CHMs are collected from an airborne platform.

The model was tasked to use the multispectral bands as input and generate the corresponding CHM.


During inference, the model used high-resolution (1m) Multispectral imagery from the National Agriculture Imagery Program (NAIP), collected during leaf-on conditions in 2023. NAIP imagery provides red, green, blue, and near infrared bands, capturing detailed vegetation patterns across the Mid-Atlantic.


Limitations include reduced accuracy in urban areas with sparse vegetation or under cloud-covered RGB inputs or out-of-distribution camera settings for the multispectral imagery.
description: Acknowledgements









NEON (National Ecological Observatory Network). Ecosystem structure (DP3.30015.001), RELEASE-2025. https://doi.org/10.48443/jqqd-1n30. Dataset accessed from https://data.neonscience.org/data-products/DP3.30015.001/RELEASE-2025 on October 3, 2025.







NEON (National Ecological Observatory Network). High-resolution orthorectified camera imagery mosaic (DP3.30010.001), RELEASE-2025. https://doi.org/10.48443/gdgn-3r69. Dataset accessed from https://data.neonscience.org/data-products/DP3.30010.001/RELEASE-2025 on October 3, 2025.







U.S. Geological Survey. National Agriculture Imagery Program (NAIP) Color-Infrared Imagery, RELEASE-2023. https://doi.org/10.5066/F7QN651G. Dataset accessed from https://earthexplorer.usgs.gov/ on October 3, 2025.