The Laboratory for Remote Sensing and Environmental Change (LRSEC)
The Laboratory for Remote Sensing and Environmental Change (LRSEC)
An Interdisciplinary Research Group Using Remote Sensing and Geospatial Science to Understand Landscape Change
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    • Dr. Gang Chen
    • Chenyu Xing
    • Ravi Thapaliya
    • Rachel Caron
    • Justin Erlick
    • Austin Barbee
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Latest News in LRSEC

  • Welcome Austin Barbee to join LRSEC October 3, 2024
  • Welcome Justin Erlick to join the lab September 16, 2024
  • Welcome Rachel to join the lab March 21, 2024

Contact Lab Director

Dr. Gang Chen
Address: McEniry 446, 9201 University City Blvd, Charlotte, NC 28223, USA (35°18'26"N 80°43'48"W)
Email: Gang.Chen 'at' charlotte.edu

Links

  • Department of Earth, Environmental and Geographical Sciences
  • College of Humanities & Earth and Social Sciences
  • University of North Carolina at Charlotte

Research Products

Product 1: UrbanWatch: A 1-meter resolution land cover and land use database for major cities in the United States

UrbanWatch is a 1-meter resolution, open-access land cover and land use (LCLU) database for major cities across the conterminous United States. UrbanWatch contains 9 LCLU classes, i.e., building, road, parking lot, tree canopy, grass/shrub, water, agriculture, barren, and others, with an overall accuracy of 91.52%. The data are open access through: https://urbanwatch.charlotte.edu/. A paper describing the details about UrbanWatch has been published in Remote Sensing of Environment.

Zhang, Y., Chen, G., Myint, S.W., Zhou, Y., Hay, G.J., Vukomanovic, J., & Meentemeyer, R.K. (2022). UrbanWatch: A 1-meter resolution land cover and land use database for 22 major cities in the United States. Remote Sensing of Environment, 278: 113106.

Figure: Examples of various types of LCLU (e.g., “A”, “B”, and “AA”) and their corresponding results in UrbanWatch (e.g., “A_1”, “B_1”, and “AA_1”), demonstrating intraclass or interclass variation.

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Product 2: Shadow Semantic Annotation Database (SSAD)

This database was developed for extracting fine-scale land cover in shadows from high-resolution remote sensing imagery. It comprises 103 image patches (500×500 pixels per patch, 1.0-meter resolution) containing various types of shadows and six major land-cover classes – building, tree, grass/shrub, road, water, and farmland. A paper describing the details about the SSAD and the use of this database has been published in Remote Sensing of Environment.

Zhang, Y., Chen, G., Vukomanovic, J., Singh K.K., Liu, Y., Holden, S., & Meentemeyer, R.K. (2020). Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping. Remote Sensing of Environment, 247, 111945.

The database is freely accessible (click HERE).

Figure: Three sample patches along an urban-rural gradient from SSAD: (a) NAIP Infrared-Red-Green image composites, (b) SSAD Category (i) results, and (c) SSAD Category (ii) results.
Copyright © 2012-2025 Gang Chen, University of North Carolina at Charlotte. All rights reserved.
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