Exploratory analysis of OpenStreetMap for land use classification

In the last years, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information. This huge amount of data might be hiding a vast geographical richness and therefore research needs to be conducted to explore their potential and use it in the solution of real world problems. In this study we conduct an exploratory analysis of data from the OpenStreetMap initiative. Using the Corine Land Cover database as reference and continental Portugal as the study area, we establish a possible correspondence between both classification nomenclatures, evaluate the quality of OpenStreetMap polygon features classification against Corine Land Cover classes from level 1 nomenclature, and analyze the spatial distribution of OpenStreetMap classes over continental Portugal. A global classification accuracy around 76% and interesting coverage areas’ values are remarkable and promising results that encourages us for future research on this topic

Quantitative analysis of anthropogenic morphologies based on multi-temporal high-resolution topography

Human activities have reshaped the geomorphology of landscapes and created vast anthropogenic geomorphic features, which have distinct characteristics compared with landforms produced by natural processes. High-resolution topography from LiDAR has opened avenues for the analysis of anthropogenic geomorphic signatures, providing new opportunities for a better understanding of Earth surface processes and landforms. However, quantitative identification and monitoring of such anthropogenic signature still represent a challenge for the Earth science community. The purpose of this contribution is to explore a method for monitoring geomorphic changes and identifying the driving forces of such changes. The study was carried out on the Eibar watershed in Spain. The proposed method is able to quantitatively detect anthropogenic geomorphic changes based on multi-temporal LiDAR topography, and it is based on a combination of two techniques: the DEM of Difference (DoD) and the Slope Local Length of Auto-correlation (SLLAC). First, we tested the capability of the SLLAC and derived parameters to distinguish different types of anthropogenic geomorphologies in 5 study case at a small scale. Second, we calculated the DoD to quantify the geomorphic changes between 2008 and 2016. Based on the proposed approach, we classified the whole basin into three categories of geomorphic changes (natural, urban or mosaic areas). The urban area had the most clustered and largest geomorphic changes, followed by the mosaic area and the natural area. This research might help to identify and monitoring anthropogenic geomorphic changes over large areas, to schedule sustainable environmental planning, and to mitigate the consequences of anthropogenic alteration

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

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

Geographies of Empty Spaces on Print and Digital Reference Maps: A Study of Washington State

J. B. Harley’s insistence that “there is no such thing as an empty space on a map” invites critical inquiry into which places are being left blank in popular reference maps, and why. I discuss the myriad reasons that items may not appear on a map, and invite a rethinking of the way selection is conceptualized in cartographic education. In this study, several GIS-supported methods are used to identify and compare consistently empty areas in print and digital maps of Washington State made by Google, Microsoft, OpenStreetMap, Rand McNally, National Geographic, and the state Department of Transportation. I then examine the physical and human landscapes of these places using imagery overlays, queries of land ownership data, and observations from site visits. In the state of Washington, empty spaces on the map are highly connected with regional and global economies, and are essential for supporting the needs of urban life such as food, electricity, construction, and waste disposal. City dwellers may not ever see or recognize the intensive land uses occurring in these geographies, whose landowners include an intriguing mix of large industries, multiple levels of government, religious colonies, and individuals searching for space and solitude

An HGIS Approach to Land-Use/Land-Cover Change in the Blanice Watershed, Czech Republic

In the South Bohemian region of the Czech Republic, the landscape is distinguished by a network of long narrow fields bordered by hedgerows clustered in small groups. These unique clusters of hedgerows have been interacting with their environment, effectively mitigating erosion, since they were first established in the High Middle Ages. In this research project I used historical maps to characterize land-use and land-cover (LULC) change relating to hedgerow features in one cadastral territory in the Blanice Watershed. Using georeferenced historical maps from 1837 and 1952, and unreferenced historical maps from 1837 to 1953, I compared the historical LULC to the current LULC within the cadastral territory of Křišťanovice. From 1837 to present-day Křišťanovice, the percentage of farmed land has decreased from 59.9% to 25.8%, while the percentage of forested area has increased from 26.6% to 61.9%. These changes reflect historical trends in land management as well as the impact of social and political changes on the environment. This project is also a methodological and epistemological exploration of a Historical GIS approach to research, and the methods developed to conduct LULC change analysis reflect these theoretical components. The results of this research provide a spatiotemporal HGIS analysis of LULC change, a workflow for applying the HGIS methods developed for this research, and a geodatabase for the storage, classification, and visualization of historical LULC data

Using OpenStreetMap to Create Land Use and Land Cover Maps

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

Robust Normalized Softmax Loss for Deep Metric Learning-Based Characterization of Remote Sensing Images With Label Noise

Most deep metric learning-based image characterization methods exploit supervised information to model the semantic relations among the remote sensing (RS) scenes. Nonetheless, the unprecedented availability of large-scale RS data makes the annotation of such images very challenging, requiring automated supportive processes. Whether the annotation is assisted by aggregation or crowd-sourcing, the RS large-variance problem, together with other important factors [e.g., geo-location/registration errors, land-cover changes, even low-quality Volunteered Geographic Information (VGI), etc.] often introduce the so-called label noise, i.e., semantic annotation errors. In this article, we first investigate the deep metric learning-based characterization of RS images with label noise and propose a novel loss formulation, named robust normalized softmax loss (RNSL), for robustly learning the metrics among RS scenes. Specifically, our RNSL improves the robustness of the normalized softmax loss (NSL), commonly utilized for deep metric learning, by replacing its logarithmic function with the negative Box–Cox transformation in order to down-weight the contributions from noisy images on the learning of the corresponding class prototypes. Moreover, by truncating the loss with a certain threshold, we also propose a truncated robust normalized softmax loss (t-RNSL) which can further enforce the learning of class prototypes based on the image features with high similarities between them, so that the intraclass features can be well grouped and interclass features can be well separated. Our experiments, conducted on two benchmark RS data sets, validate the effectiveness of the proposed approach with respect to different state-of-the-art methods in three different downstream applications (classification, clustering, and retrieval). The codes of this article will be publicly available from https://github.com/jiankang1991

Enhancing Data Classification Quality of Volunteered Geographic Information

Geographic data is one of the fundamental components of any Geographic Information System (GIS). Nowadays, the utility of GIS becomes part of everyday life activities, such as searching for a destination, planning a trip, looking for weather information, etc. Without a reliable data source, systems will not provide guaranteed services. In the past, geographic data was collected and processed exclusively by experts and professionals. However, the ubiquity of advanced technology results in the evolution of Volunteered Geographic Information (VGI), when the geographic data is collected and produced by the general public. These changes influence the availability of geographic data, when common people can work together to collect geographic data and produce maps. This particular trend is known as collaborative mapping. In collaborative mapping, the general public shares an online platform to collect, manipulate, and update information about geographic features. OpenStreetMap (OSM) is a prominent example of a collaborative mapping project, which aims to produce a free world map editable and accessible by anyone. During the last decade, VGI has expanded based on the power of crowdsourcing. The involvement of the public in data collection raises great concern about the resulting data quality. There exist various perspectives of geographic data quality this dissertation focuses particularly on the quality of data classification (i.e., thematic accuracy). In professional data collection, data is classified based on quantitative and/or qualitative ob- servations. According to a pre-defined classification model, which is usually constructed by experts, data is assigned to appropriate classes. In contrast, in most collaborative mapping projects data classification is mainly based on individualsa cognition. Through online platforms, contributors collect information about geographic features and trans- form their perceptions into classified entities. In VGI projects, the contributors mostly have limited experience in geography and cartography. Therefore, the acquired data may have a questionable classification quality. This dissertation investigates the challenges of data classification in VGI-based mapping projects (i.e., collaborative mapping projects). In particular, it lists the challenges relevant to the evolution of VGI as well as to the characteristics of geographic data. Furthermore, this work proposes a guiding approach to enhance the data classification quality in such projects. The proposed approach is based on the following premises (i) the availability of large amounts of data, which fosters applying machine learning techniques to extract useful knowledge, (ii) utilization of the extracted knowledge to guide contributors to appropriate data classification, (iii) the humanitarian spirit of contributors to provide precise data, when they are supported by a guidance system, and (iv) the power of crowdsourcing in data collection as well as in ensuring the data quality. This cumulative dissertation consists of five peer-reviewed publications in international conference proceedings and international journals. The publications divide the disser- tation into three parts the first part presents a comprehensive literature review about the relevant previous work of VGI quality assurance procedures (Chapter 2), the second part studies the foundations of the approach (Chapters 3-4), and the third part discusses the proposed approach and provides a validation example for implementing the approach (Chapters 5-6). Furthermore, Chapter 1 presents an overview about the research ques- tions and the adapted research methodology, while Chapter 7 concludes the findings and summarizes the contributions. The proposed approach is validated through empirical studies and an implemented web application. The findings reveal the feasibility of the proposed approach. The output shows that applying the proposed approach results in enhanced data classification quality. Furthermore, the research highlights the demands for intuitive data collection and data interpretation approaches adequate to VGI-based mapping projects. An interaction data collection approach is required to guide the contributors toward enhanced data quality, while an intuitive data interpretation approach is needed to derive more precise information from rich VGI resources

Quality Assessment of the Canadian OpenStreetMap Road Networks

Volunteered geographic information (VGI) has been applied in many fields such as participatory planning, humanitarian relief and crisis management because of its cost-effectiveness. However, coverage and accuracy of VGI cannot be guaranteed. OpenStreetMap (OSM) is a popular VGI platform that allows users to create or edit maps using GPS-enabled devices or aerial imageries. The issue of geospatial data quality in OSM has become a trending research topic because of the large size of the dataset and the multiple channels of data access. The objective of this study is to examine the overall reliability of the Canadian OSM data. A systematic review is first presented to provide details on the quality evaluation process of OSM. A case study of London, Ontario is followed as an experimental analysis of completeness, positional accuracy and attribute accuracy of the OSM street networks. Next, a national study of the Canadian OSM data assesses the overall semantic accuracy and lineage in addition to the quality measures mentioned above. Results of the quality evaluation are compared with associated OSM provenance metadata to examine potential correlations. The Canadian OSM road networks were found to have comparable accuracy with the tested commercial database (DMTI). Although statistical analysis suggests that there are no significant relations between OSM accuracy and its editing history, the study presents the complex processes behind OSM contributions possibly influenced by data import and remote mapping. The findings of this thesis can potentially guide cartographic product selection for interested parties and offer a better understanding of future quality improvement in OSM