Automated identification and characterization of parcels with OpenStreetMap and points of interest

Abstract

Against the paucity of information on urban parcels in China, we propose a method to automatically identify and characterize parcels using OpenStreetMap (OSM) and points of interest (POI) data. Parcels are the basic spatial units for fine-scale urban modeling, urban studies, and spatial planning. Conventional methods for identification and characterization of parcels rely on remote sensing and field surveys, which are labor intensive and resource consuming. Poorly developed digital infrastructure, limited resources, and institutional barriers have all hampered the gathering and application of parcel data in China. Against this backdrop, we employ OSM road networks to identify parcel geometries and POI data to infer parcel characteristics. A vector-based cellular automata model is adopted to select urban parcels. The method is applied to the entire state of China and identifies 82 645 urban parcels in 297 cities. Notwithstanding all the caveats of open and/or crowd-sourced data, our approach can produce a reasonably good approximation of parcels identified using conventional methods, thus it has the potential to become a useful tool.

The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China

“…The first one is to use existing fields, such as census blocks or land parcels [2,7,32]. While these existing fields are usually detailed and reliable, they are usually inaccessible (and often confidential) in China [8]. Furthermore, they cannot meet the requirements of the routine update of urban land use maps, because only the land use type can be updated, instead of field boundary change and land parcel integration or separation.…”

“…Furthermore, they cannot meet the requirements of the routine update of urban land use maps, because only the land use type can be updated, instead of field boundary change and land parcel integration or separation. Traditional visual interpretation and manual digitization is another way to obtain the fields [14], yet is time-and labor-consuming, and may lead to inconsistent field boundaries [7,8].…”

“…In this paper, the OSM road network was used to segment the study area into disaggregated fields: urban land use parcels with homogeneous socioeconomic functions. It has been demonstrated that fields automatically segmented by road networks can yield satisfactory per-field classification results [8,20,27].…”

“…A method proposed by Liu and Long [8] was adopted in this paper to generate the urban land use parcels. Because OSM roads rely on crowdsourced contribution, their completeness is heterogeneous across space [34].…”

“…Traditionally, the update of land use maps has mainly relied on experienced analysts interpreting remote sensing images, topographic maps, and building maps with the assistance of field survey and statistical data [6,7]. While this is laborand time-consuming, desirable and consistent land use information cannot be guaranteed due to the various judgements and technical proficiency levels of analysts [4,7,8]. In sum, as a great challenge to urban land administration and planning, the derivation of fine-grained, accurate, and timely urban land use information is needed to keep up with the pace of urban development, and to better assist municipal administrations with their routine land use mapping assignments.…”