Open land cover from OpenStreetMap and remote sensing

Cities are particularly vulnerable to meteorological hazards because of the concentration of population, goods, capital stock and infrastructure. Urban climate services require multi-disciplinary and multi-sectorial approaches and new paradigms in urban climate modelling. This paper classifies the required urban input data for both mesoscale state-of-the-art Urban Canopy Models (UCMs) and microscale Obstacle Resolving Models (ORM) into five categories and reviews the ways in which they can be obtained. The first two categories are (1) land cover, and (2) building morphology. These govern the main interactions between the city and the urban climate and the Urban Heat Island. Interdependence between morphological parameters and UCM geometric hypotheses are discussed. Building height, plan and wall area densities are recommended as the main input variables for UCMs, whereas ORMs require 3D building data. Recently, three other categories of urban data became relevant for finer urban studies and adaptation to climate change: (3) building design and architecture, (4) building use, anthropogenic heat and socio-economic data, and (5) urban vegetation data. Several methods for acquiring spatial information are reviewed, including remote sensing, geographic information system (GIS) processing from administrative cadasters, expert knowledge and crowdsourcing. Data availability, data harmonization, costs/efficiency trade-offs and future challenges are then discussed.

City-descriptive input data for urban climate models: Model requirements, data sources and challenges

Land-cover mapping is one of the foundations of Earth science As a result of the combined efforts of many scientists, numerous global land-cover (GLC) products with a resolution of 30 m have so far been generated However, the increasing number of fine-resolution GLC datasets is imposing additional workloads as it is necessary to confirm the quality of these datasets and check their suitability for user applications To provide guidelines for users, in this study, the recent developments in currently available 30 m GLC products (including three GLC products and thematic products for four different land-cover types, ie, impervious surface, forest, cropland, and inland water) were first reviewed Despite the great efforts toward improving mapping accuracy that there have been in recent decades, the current 30 m GLC products still suffer from having relatively low accuracies of between 460% and 889% for GlobeLand30-2010, 5771% and 8036% for FROM_GLC-2015, and 6559% and 8433% for GLC_FCS30-2015 The reported accuracies for the global 30 m thematic maps vary from 6786% to 951% for the eight impervious surface products that were reviewed, 5672% to 9736% for the seven forest products, 3273% to 983% for the six cropland products, and 1567% to 997% for the six inland water products The consistency between the current GLC products was then examined The GLC maps showed a good overall agreement in terms of spatial patterns but a limited agreement for some vegetation classes (such as shrub, tree, and grassland) in specific areas such as transition zones Finally, the prospects for fine-resolution GLC mapping were also considered With the rapid development of cloud computing platforms and big data, the Google Earth Engine (GEE) greatly facilitates the production of global fine-resolution land-cover maps by integrating multisource remote sensing datasets with advanced image processing and classification algorithms and powerful computing capability The synergy between the spectral, spatial, and temporal features derived from multisource satellite datasets and stored in cloud computing platforms will definitely improve the classification accuracy and spatiotemporal resolution of fine-resolution GLC products In general, up to now, most land-cover maps have not been able to achieve the maximum (per class or overall) error of 5%–15% required by many applications Therefore, more efforts are needed toward improving the accuracy of these GLC products, especially for classes for which the accuracy has so far been low (such as shrub, wetland, tundra, and grassland) and in terms of the overall quality of the maps

Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

Up-to-date and reliable land-use information is essential for a variety of applications such as planning or monitoring of the urban environment. This research presents a workflow for mapping urban land use at the street block level, with a focus on residential use, using very-high resolution satellite imagery and derived land-cover maps as input. We develop a processing chain for the automated creation of street block polygons from OpenStreetMap and ancillary data. Spatial metrics and other street block features are computed, followed by feature selection that reduces the initial datasets by more than 80%, providing a parsimonious, discriminative, and redundancy-free set of features. A random forest (RF) classifier is used for the classification of street blocks, which results in accuracies of 84% and 79% for five and six land-use classes, respectively. We exploit the probabilistic output of RF to identify and relabel blocks that have a high degree of uncertainty. Finally, the thematic precision of the residential blocks is refined according to the proportion of the built-up area. The output data and processing chains are made freely available. The proposed framework is able to process large datasets, given that the cities in the case studies, Dakar and Ouagadougou, cover more than 1000 km2 in total, with a spatial resolution of 0.5 m.

Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics

In this work, we investigate the use of OpenStreetMap data for semantic labeling of Earth Observation images Deep neural networks have been used in the past for remote sensing data classification from various sensors, including multispectral, hyperspectral, SAR and LiDAR data While OpenStreetMap has already been used as ground truth data for training such networks, this abundant data source remains rarely exploited as an input information layer In this paper, we study different use cases and deep network architectures to leverage OpenStreetMap data for semantic labeling of aerial and satellite images Especially, we look into fusion based architectures and coarseto- fine segmentation to include the OpenStreetMap layer into multispectral-based deep fully convolutional networks We illustrate how these methods can be successfully used on two public datasets: ISPRS Potsdam and DFC2017 We show that OpenStreetMap data can efficiently be integrated into the vision-based deep learning models and that it significantly improves both the accuracy performance and the convergence speed of the networks

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Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps

With the opening up of the Landsat archive, global high resolution land cover maps have begun to appear. However, they often have only a small number of high level land cover classes and they are static products, corresponding to a particular period of time, e.g., the GlobeLand30 (GL30) map for 2010. The OpenStreetMap (OSM), in contrast, consists of a very detailed, dynamically updated, spatial database of mapped features from around the world, but it suffers from incomplete coverage, and layers of overlapping features that are tagged in a variety of ways. However, it clearly has potential for land use and land cover (LULC) mapping. Thus the aim of this paper is to demonstrate how the OSM can be converted into a LULC map and how this OSM-derived LULC map can then be used to first update the GL30 with more recent information and secondly, enhance the information content of the classes. The technique is demonstrated on two study areas where there is availability of OSM data but in locations where authoritative data are lacking, i.e., Kathmandu, Nepal and Dar es Salaam, Tanzania. The GL30 and its updated and enhanced versions are independently validated using a stratified random sample so that the three maps can be compared. The results show that the updated version of GL30 improves in terms of overall accuracy since certain classes were not captured well in the original GL30 (e.g., water in Kathmandu and water/wetlands in Dar es Salaam). In contrast, the enhanced GL30, which contains more detailed urban classes, results in a drop in the overall accuracy, possibly due to the increased number of classes, but the advantages include the appearance of more detailed features, such as the road network, that becomes clearly visible.

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Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30

OpenStreetMap (OSM) is a bottom up community-driven initiative to create a global map of the world. Yet the application of OSM to land use and land cover (LULC) mapping is still largely unexploited due to problems with inconsistencies in the data and harmonization of LULC nomenclatures with OSM. This chapter outlines an automated methodology for creating LULC maps using the nomenclature of two European LULC products: the Urban Atlas (UA) and CORINE Land Cover (CLC). The method is applied to two regions in London and Paris. The results show that LULC maps with a level of detail similar to UA can be obtained for the urban regions, but that OSM has limitations for conversion into the more detailed non-urban classes of the CLC nomenclature. Future work will concentrate on developing additional rules to improve the accuracy of the transformation and building an online system for processing the data.

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Using OpenStreetMap to Create Land Use and Land Cover Maps: Development of an Application

The appearance of OpenStreetMap (OSM) in 2004 sparked a phenomenon known as Volunteered Geographic Information (VGI) Today, VGI comes in many flavours (eg toponyms, GPS tracks, geo-tagged photos, micro-blogging or complete topographic maps) and from various sources One subject that has attracted research interest from the early days of VGI is how good such datasets are and how to combine them with authoritative datasets To this end, the paper explores three intertwined subjects from a quality point of view First, we examine the topo-semantic consistency of OSM data by evaluating a number of rules between polygonal and linear features and then paying special attention to quality of Points of Interest (POIs) A number of topo-semantic rules will be used to evaluate the valididy of features’ location The focus then turns to the use of geo-tagged photos to evaluate the location and type of OSM data and to disambiguate topological issues that arise when different OSM layers overlap

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An automated methodology for converting OSM data into a Land Use/Cover map

. This paper examines the feasibility of using data from OpenStreetMap (OSM), Facebook and Foursquare as a source of information on the function of buildings. Such information is rarely openly available and if available, would vary between cities by nomenclature, making comparisons between places difficult. Volunteered Geographic Information (VGI) including data from social media represents new potential sources of building function data that have not yet been exploited for this purpose. Using a part of the city of Milan as the study area, building data from OSM and points of interest (POIs) from OSM, Facebook and Foursquare were extracted to derive the building function. This resulted in the classification of building function for more than 80 % of the buildings and demonstrated that both Facebook and Foursquare can complement the building function derived from OSM, helping to fill in missing gaps. This preliminary study has demonstrated the potential of this approach for deriving building function information from open data in a simple way yet still requires independent validation with alternative sources as well as extension to other areas that have different amounts of OSM and social media coverage.

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Classification of Building Function Using Available Sources of Vgi

OSM2LULC is a software package developed to automatically convert OpenStreetMap (OSM) data into Land Use Land Cover (LULC) maps using Free and Open Source Software for Geospatial (FOSS4G) tools. It needs to be highly efficient given the increasing detail of OSM data and the need to apply it to large extent regions. In this article, a comparison between the implementation of OSM2LULC in different available GIS platforms is made using both vector and raster data structures, which resulted in different versions. A description of the differences of each version is made and, to assess their performance, they were applied to four different study areas with different characteristics, in terms of available OSM data and area size. The performance of each version was evaluated taking into account: the overall processing time required to obtain LULC maps; and differences in the results obtained when different data structures (vector and raster) were used. Results showed that the adoption of a strategy that favors interoperability between FOSS4G and the combined use of both vector and raster data promotes a performance increase. After analysing the topological relationships of OSM data, the conversion to raster data format and the execution of procedural parts with such data indicated significant performance gains, without any positional distortions that significantly compromise the applicability of the final result in further case scenarios.

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Joaquim Patriarca

Papers

Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30

Using OpenStreetMap to Create Land Use and Land Cover Maps: Development of an Application

An automated methodology for converting OSM data into a Land Use/Cover map

Classification of Building Function Using Available Sources of Vgi

Automatic conversion of OSM data into LULC maps: comparing FOSS4G based approaches towards an enhanced performance