UrbanWatch: A 1-meter resolution land cover and land use database for 22 major cities in the United States

Yindan Zhang, Gang Chen, Soe W. Myint, Yuyu Zhou, Geoffrey J. Hay, Jelena Vukomanovic, Ross K. Meentemeyer

Research output: Contribution to journalArticlepeer-review

Abstract

Very-high-resolution (VHR) land cover and land use (LCLU) is an essential baseline data for understanding fine-scale interactions between humans and the heterogeneous landscapes of urban environments. In this study, we developed a Fine-resolution, Large-area Urban Thematic information Extraction (FLUTE) framework to address multiple challenges facing large-area, high-resolution urban mapping, including the view angle effect, high intraclass and low interclass variation, and multiscale land cover types. FLUTE builds upon a teacher-student deep learning architecture, and includes two new feature extraction modules – Scale-aware Parsing Module (SPM) and View-aware Embedding Module (VEM). Our model was trained with a new benchmark database containing 52.43 million labeled pixels (from 2014 to 2017 NAIP airborne Imagery) to capture diverse LCLU types and spatial patterns. We assessed the credibility of FLUTE by producing a 1-meter resolution database named UrbanWatch for 22 major cities across the conterminous United States. UrbanWatch contains nine LCLU classes – building, road, parking lot, tree canopy, grass/shrub, water, agriculture, barren, and others, with an overall accuracy of 91.52%. We have further made UrbanWatch freely accessible to support urban-related research, urban planning and management, and community outreach efforts: https://urbanwatch.charlotte.edu.

Original languageEnglish (US)
Article number113106
JournalRemote Sensing of Environment
Volume278
DOIs
StatePublished - Sep 1 2022

Keywords

  • Deep learning
  • Land cover and land use (LCLU)
  • Open access
  • Urban
  • UrbanWatch
  • Very high resolution (VHR)

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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