Scholarlink

<p>Manufactured housing communities (MHCs) are residential neighborhoods consisting mainly of factory-built homes, providing affordable housing for economically disadvantaged households. There are few, if any, publicly available, systematically created datasets that detail unit-level characteristics of MHCs, e.g., unit location, boundaries, footprint. This article describes a dataset of manufactured housing parks—where multiple units are situated on a single rented parcel—collected from the State of Wisconsin. The dataset is intended to support applications in urban planning, policymaking, and disaster response. The dataset was produced using high-resolution aerial imagery and computer vision techniques to systematically detect and map MHCs. Data collection began by dividing the study area into square tiles for systematic detection. Detected communities were then vectorized, overlaid on statewide parcel maps, and processed to extract relevant attributes. The dataset includes both tabular and geospatial formats, documenting park distribution, spatial extent, and housing unit counts. This dataset provides a reproducible, quality-controlled resource designed for broad reuse across disciplines.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Aerial Imagery-Derived Dataset of Manufactured Housing Communities in the North Central United States

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月29日

查看全文
  • Armin Yeganeh,
  • Maria Marshall,
  • Noah Durst

摘要

Manufactured housing communities (MHCs) are residential neighborhoods consisting mainly of factory-built homes, providing affordable housing for economically disadvantaged households. There are few, if any, publicly available, systematically created datasets that detail unit-level characteristics of MHCs, e.g., unit location, boundaries, footprint. This article describes a dataset of manufactured housing parks—where multiple units are situated on a single rented parcel—collected from the State of Wisconsin. The dataset is intended to support applications in urban planning, policymaking, and disaster response. The dataset was produced using high-resolution aerial imagery and computer vision techniques to systematically detect and map MHCs. Data collection began by dividing the study area into square tiles for systematic detection. Detected communities were then vectorized, overlaid on statewide parcel maps, and processed to extract relevant attributes. The dataset includes both tabular and geospatial formats, documenting park distribution, spatial extent, and housing unit counts. This dataset provides a reproducible, quality-controlled resource designed for broad reuse across disciplines.