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1. Paved or unpaved? A Deep Learning derived Road Surface Global Dataset from Mapillary Street-View Imagery

Author

Randhawa, Sukanya, Aygun, Eren, Randhawa, Guntaj, Herfort, Benjamin, Lautenbach, Sven, and Zipf, Alexander

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

We have released an open dataset with global coverage on road surface characteristics (paved or unpaved) derived utilising 105 million images from the world's largest crowdsourcing-based street view platform, Mapillary, leveraging state-of-the-art geospatial AI methods. We propose a hybrid deep learning approach which combines SWIN-Transformer based road surface prediction and CLIP-and-DL segmentation based thresholding for filtering of bad quality images. The road surface prediction results have been matched and integrated with OpenStreetMap (OSM) road geometries. This study provides global data insights derived from maps and statistics about spatial distribution of Mapillary coverage and road pavedness on a continent and countries scale, with rural and urban distinction. This dataset expands the availability of global road surface information by over 3 million kilometers, now representing approximately 36% of the total length of the global road network. Most regions showed moderate to high paved road coverage (60-80%), but significant gaps were noted in specific areas of Africa and Asia. Urban areas tend to have near-complete paved coverage, while rural regions display more variability. Model validation against OSM surface data achieved strong performance, with F1 scores for paved roads between 91-97% across continents. Taking forward the work of Mapillary and their contributors and enrichment of OSM road attributes, our work provides valuable insights for applications in urban planning, disaster routing, logistics optimisation and addresses various Sustainable Development Goals (SDGS): especially SDGs 1 (No poverty), 3 (Good health and well-being), 8 (Decent work and economic growth), 9 (Industry, Innovation and Infrastructure), 11 (Sustainable cities and communities), 12 (Responsible consumption and production), and 13 (Climate action).

Published
2024

2. Deep Learning Enhanced Road Traffic Analysis: Scalable Vehicle Detection and Velocity Estimation Using PlanetScope Imagery

Author

Adamiak, Maciej, Grinblat, Yulia, Psotta, Julian, Fulman, Nir, Mazumdar, Himshikhar, Tang, Shiyu, and Zipf, Alexander

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

This paper presents a method for detecting and estimating vehicle speeds using PlanetScope SuperDove satellite imagery, offering a scalable solution for global vehicle traffic monitoring. Conventional methods such as stationary sensors and mobile systems like UAVs are limited in coverage and constrained by high costs and legal restrictions. Satellite-based approaches provide broad spatial coverage but face challenges, including high costs, low frame rates, and difficulty detecting small vehicles in high-resolution imagery. We propose a Keypoint R-CNN model to track vehicle trajectories across RGB bands, leveraging band timing differences to estimate speed. Validation is performed using drone footage and GPS data covering highways in Germany and Poland. Our model achieved a Mean Average Precision of 0.53 and velocity estimation errors of approximately 3.4 m/s compared to GPS data. Results from drone comparison reveal underestimations, with average speeds of 112.85 km/h for satellite data versus 131.83 km/h from drone footage. While challenges remain with high-speed accuracy, this approach demonstrates the potential for scalable, daily traffic monitoring across vast areas, providing valuable insights into global traffic dynamics.

Published
2024

3. Distortions in Judged Spatial Relations in Large Language Models

Author

Fulman, Nir, Memduhoğlu, Abdulkadir, and Zipf, Alexander

Subjects
Computer Science - Computation and Language
Abstract

We present a benchmark for assessing the capability of Large Language Models (LLMs) to discern intercardinal directions between geographic locations and apply it to three prominent LLMs: GPT-3.5, GPT-4, and Llama-2. This benchmark specifically evaluates whether LLMs exhibit a hierarchical spatial bias similar to humans, where judgments about individual locations' spatial relationships are influenced by the perceived relationships of the larger groups that contain them. To investigate this, we formulated 14 questions focusing on well-known American cities. Seven questions were designed to challenge the LLMs with scenarios potentially influenced by the orientation of larger geographical units, such as states or countries, while the remaining seven targeted locations were less susceptible to such hierarchical categorization. Among the tested models, GPT-4 exhibited superior performance with 55 percent accuracy, followed by GPT-3.5 at 47 percent, and Llama-2 at 45 percent. The models showed significantly reduced accuracy on tasks with suspected hierarchical bias. For example, GPT-4's accuracy dropped to 33 percent on these tasks, compared to 86 percent on others. However, the models identified the nearest cardinal direction in most cases, reflecting their associative learning mechanism, thereby embodying human-like misconceptions. We discuss avenues for improving the spatial reasoning capabilities of LLMs., Comment: This manuscript has been accepted for publication in The Professional Geographer

Published
2024

6. Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks

Author

Hong, Danfeng, Zhang, Bing, Li, Hao, Li, Yuxuan, Yao, Jing, Li, Chenyu, Werner, Martin, Chanussot, Jocelyn, Zipf, Alexander, and Zhu, Xiao Xiang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e.g., single cities or regions). Yet these AI models tend to meet the performance bottleneck in the case studies across cities or regions, due to the lack of diverse RS information and cutting-edge solutions with high generalization ability. To this end, we build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task (called C2Seg dataset), which consists of two cross-city scenes, i.e., Berlin-Augsburg (in Germany) and Beijing-Wuhan (in China). Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN for short, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion but also closing the gap derived from enormous differences of RS image representations between different cities by means of adversarial learning. In addition, the Dice loss is considered in HighDAN to alleviate the class imbalance issue caused by factors across cities. Extensive experiments conducted on the C2Seg dataset show the superiority of our HighDAN in terms of segmentation performance and generalization ability, compared to state-of-the-art competitors. The C2Seg dataset and the semantic segmentation toolbox (involving the proposed HighDAN) will be available publicly at https://github.com/danfenghong.

Published
2023

7. Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation

Author

Li, Hao, Yuan, Zhendong, Dax, Gabriel, Kong, Gefei, Fan, Hongchao, Zipf, Alexander, and Werner, Martin

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Accurate building height estimation is key to the automatic derivation of 3D city models from emerging big geospatial data, including Volunteered Geographical Information (VGI). However, an automatic solution for large-scale building height estimation based on low-cost VGI data is currently missing. The fast development of VGI data platforms, especially OpenStreetMap (OSM) and crowdsourced street-view images (SVI), offers a stimulating opportunity to fill this research gap. In this work, we propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OSM data to generate low-cost and open-source 3D city modeling in LoD1. The proposed method consists of three parts: first, we propose an SSL schema with the option of setting a different ratio of "pseudo label" during the supervised regression; second, we extract multi-level morphometric features from OSM data (i.e., buildings and streets) for the purposed of inferring building height; last, we design a building floor estimation workflow with a pre-trained facade object detection network to generate "pseudo label" from SVI and assign it to the corresponding OSM building footprint. In a case study, we validate the proposed SSL method in the city of Heidelberg, Germany and evaluate the model performance against the reference data of building heights. Based on three different regression models, namely Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the SSL method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters, which is competitive to state-of-the-art approaches. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data, with possibilities in even regions and areas with diverse data quality and availability., Comment: Accepted for GIScience 2023

Published
2023

20. An investigation of the temporality of OpenStreetMap data contribution activities.

Author

Novack, Tessio, Vorbeck, Leonard, and Zipf, Alexander

Subjects
MOVING average process, TIME series analysis, CITIES & towns, BOX-Jenkins forecasting
Abstract

OpenStreetMap (OSM) is a dataset in constant change and this dynamic needs to be better understood. Based on 12-year time series of seven OSM data contribution activities extracted from 20 large cities worldwide, we investigate the temporal dynamic of OSM data production, more specifically, the auto- and cross-correlation, temporal trend, and annual seasonality of these activities. Furthermore, we evaluate and compare nine different temporal regression methods for forecasting such activities in horizons of 1–4 weeks. Several insights could be obtained from our analyses, including that the contribution activities tend to grown linearly in a moderate intra-annual cycle. Also, the performance of the temporal forecasting methods shows that they yield in general more accurate estimations of future contribution activities than a baseline metric, i.e. the arithmetic average of recent previous observations. In particular, the well-known ARIMA and the exponentially weighted moving average methods have shown the best performances. [ABSTRACT FROM AUTHOR]

Published
2024
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22. OpenStreetMap zur Ermittlung von Landnutzungs- und Landbedeckungsänderungen und Schätzung dadurch erzeugter CO2-Emissionen

Author

Ulrich, Veit, Lautenbach, Sven, and Zipf, Alexander

Subjects
DFNS 2023
Abstract

Räumlich explizite Informationen zu CO2-Emissionen im Zusammenhang mit Landnutzungs- und Landbedeckungsänderungen sind für die Umsetzung lokaler Strategien zur Eindämmung des Klimawandels von entscheidender Bedeutung. Schätzungen dieser Emissionen werden jedoch meist mit einer groben räumlichen Auflösung durchgeführt, während lokale Emissionen oft nicht erfasst werden. Karten, die diese Emissionen in hoher räumlicher Auflösung zeigen, repräsentieren die lokale Dynamik von Landnutzungsänderungen besser und können die Verlässlichkeit von CO2-Fußabdruckstudien erhöhen. Obgleich viele Landbedeckungsklassifikationen wie z.B. CORINE Land Cover verfügbar sind, hat die Ableitung der Landnutzung aus freien Geodatenquellen wie z.B. OpenStreetMap den Vorteil, dass diese großräumig skalierbar und meist auf dem neuesten Stand sind. Wir haben basierend auf OpenStreetMap unter Anwendung zweier Korrekturverfahren Landnutzungsänderungen in Baden-Württemberg ermittelt und die dadurch verursachten CO2-Emissionen quantifiziert. Die ermittelten Landnutzungsänderungen haben wir anschließend mit dem Produkt „Landschaftsveränderungsdienst“ (LaVerDi) des Bundesamtes für Kartographie und Geodäsie verglichen. Dabei haben wir Landnutzungsänderungen zwischen den Klassen Siedlung, Ackerland, Weide und Wald, die zu CO2-Emissionen führen, berücksichtigt, wobei wir eine Abdeckung von 77,8 % der Fläche Baden-Württembergs erreichten. Die verschiedenen Korrekturverfahren führten zu großen Unterschieden in der Fläche der ermittelten Landnutzungsänderungen, was eine große Spannbreite an Schätzungen der CO2-Emissionen zur Folge hatte. Für ganz Baden-Württemberg betrugen die Emissionsschätzungen mit den zwei verschiedenen Korrekturverfahren 291.710 Mg C Jahr-1 bzw. 93.591 Mg C Jahr-1. Dies entspricht in etwa anderen Bruttoemissionsschätzungen für Baden-Württemberg. Gegenüber vergleichbaren Schätzungen bietet unsere Methode jedoch den Vorteil einer hohen räumlichen Auflösung. Unsicherheiten entstanden u.a. durch die unvollständige Kartierung der Landnutzung in OpenStreetMap sowie Änderungen und Korrekturen in OpenStreetMap währen des Untersuchungszeitraums. Insgesamt zeigten die Ergebnisse, dass OpenStreetMap erfolgreich zur Schätzung von Landnutzungsänderungen verwendet werden kann, wenn die extrahierten Geodaten mit den vorgeschlagenen Methoden prozessiert werden. Die hierdurch gewonnenen Informationen stellen eine vielversprechende Grundlage für die räumlich hoch aufgelöste Schätzung von Emissionen durch Landnutzungs- und Landbedeckungsänderungen dar.

Published
2023
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23. Exploring MapSwipe as a Crowdsourcing Tool for (Rapid) Damage Assessment: The Case of the 2021 Haiti Earthquake

Author

Groß, Simon, Herfort, Benjamin, Marx, Sabrina, and Zipf, Alexander

Abstract

Fast and reliable geographic information is vital in disaster management. In the late 2000s, crowdsourcing emerged as a powerful method to provide this information. Base mapping through crowdsourcing is already well-established in relief workflows. However, crowdsourced post-disaster damage assessment is researched but not yet institutionalized. Based on MapSwipe, an established mobile application for crowdsourced base mapping, a damage assessment approach was developed and tested for a case study after the 2021 Haiti earthquake. First, MapSwipe’s damage mapping results are assessed for quality by using a reference dataset in regard to different aggregation methods. Then, the MapSwipe data was compared to an already established rapid damage assessment method by the Copernicus Emergency Management Service (CEMS). Crowdsourced building damage mapping achieved a maximum F1-score of 0.63 in comparison to the reference data set. MapSwipe and CEMS data showed only slight agreement with Cohen’s Kappa values reaching a maximum of 0.16. The results highlight the potential of crowdsourcing damage assessment as well as the importance for a scientific evaluation of the quality of CEMS data. Next steps for further integrating the presented workflow into MapSwipe are discussed.

Published
2023

25. Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation

Author

Hao Li and Zhendong Yuan and Gabriel Dax and Gefei Kong and Hongchao Fan and Alexander Zipf and Martin Werner, Li, Hao, Yuan, Zhendong, Dax, Gabriel, Kong, Gefei, Fan, Hongchao, Zipf, Alexander, Werner, Martin, Hao Li and Zhendong Yuan and Gabriel Dax and Gefei Kong and Hongchao Fan and Alexander Zipf and Martin Werner, Li, Hao, Yuan, Zhendong, Dax, Gabriel, Kong, Gefei, Fan, Hongchao, Zipf, Alexander, and Werner, Martin

Abstract

Accurate building height estimation is key to the automatic derivation of 3D city models from emerging big geospatial data, including Volunteered Geographical Information (VGI). However, an automatic solution for large-scale building height estimation based on low-cost VGI data is currently missing. The fast development of VGI data platforms, especially OpenStreetMap (OSM) and crowdsourced street-view images (SVI), offers a stimulating opportunity to fill this research gap. In this work, we propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OSM data to generate low-cost and open-source 3D city modeling in LoD1. The proposed method consists of three parts: first, we propose an SSL schema with the option of setting a different ratio of "pseudo label" during the supervised regression; second, we extract multi-level morphometric features from OSM data (i.e., buildings and streets) for the purposed of inferring building height; last, we design a building floor estimation workflow with a pre-trained facade object detection network to generate "pseudo label" from SVI and assign it to the corresponding OSM building footprint. In a case study, we validate the proposed SSL method in the city of Heidelberg, Germany and evaluate the model performance against the reference data of building heights. Based on three different regression models, namely Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the SSL method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters, which is competitive to state-of-the-art approaches. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data, with possibilities in even regions and areas with diverse data quality and availability.

Published
2023
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31. Returning the favor - Leveraging quality insights of OpenStreetMap-based land-use/land-cover multi-label modeling to the community

Author

Schott, Moritz, Zell, Adina, Lautenbach, Sven, Demir, Begüm, and Zipf, Alexander

Subjects
giscience, geographic information science, research, land cover, openstreetmap
Abstract

Schott, M., Zell, A., Lautenbach, S., Demir, B., & Zipf, A. (2022). Returning the favor - Leveraging quality insights of OpenStreetMap- based land-use/land-cover multi-label modeling to the community. In: Minghini, M., Liu, P., Li, H., Grinberger, A.Y., & Juhász, L. (Eds.). Proceedings of the Academic Track at State of the Map 2022, Florence, Italy, 19-21 August 2022. Available at https://zenodo.org/communities/sotm-22

Published
2022
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32. Inequalities in the completeness of OpenStreetMap buildings in urban centers

Author

Herfort, Benjamin, Lautenbach, Sven, Porto de Albuquerque, João, Anderson, Jennings, and Zipf, Alexander

Subjects
giscience, geographic information science, research, openstreetmap, buildings
Abstract

Herfort, B., Lautenbach, S., Porto de Albuquerque, J., Anderson, J., & Zipf, A. (2022). Inequalities in the completeness of OpenStreetMap buildings in urban centers. In: Minghini, M., Liu, P., Li, H., Grinberger, A.Y., & Juhász, L. (Eds.). Proceedings of the Academic Track at State of the Map 2022, Florence, Italy, 19-21 August 2022. Available at https://zenodo.org/communities/sotm-22

Published
2022
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34. Urban Heat Island Intensity Prediction in the Context of Heat Waves: An Evaluation of Model Performance †.

Author

Martinez-Soto, Aner, Fürle, Johannes, and Zipf, Alexander

Subjects
URBAN heat islands, HEAT waves (Meteorology), HIGH temperatures, DATA analysis, PREDICTION models
Abstract

Urban heat islands, characterized by higher temperatures in cities compared to surrounding areas, have been studied using various techniques. However, during heat waves, existing models often underestimate the intensity of these heat islands compared to empirical measurements. To address this, an hourly time-series-based model for predicting heat island intensity during heat wave conditions is proposed. The model was developed and validated using empirical data from the National Monitoring Network in Temuco, Chile. Results indicate a strong correlation (r > 0.98) between the model's predictions and actual monitoring data. Additionally, the study emphasizes the importance of considering the unique microclimatic characteristics and built environment of each city when modelling urban heat islands. Factors such as urban morphology, land cover, and anthropogenic heat emissions interact in complex ways, necessitating tailored modelling approaches for the accurate representation of heat island phenomena. [ABSTRACT FROM AUTHOR]

Published
2023
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37. The association of stress and physical activity

Author

Reichert, Markus (Dr. phil.), Brüßler, Sarah, Reinhard, Iris (Dr. sc. hum.), Braun, Urs (Dr. med.), Giurgiu, Marco (Dr. phil.), Hoell, Andreas (Dr. sc. hum.), Zipf, Alexander (Prof. Dr.), Meyer-Lindenberg, Andreas (Prof. Dr. med.), Tost, Heike (Prof. Dr. Dr.), and Ebner-Priemer, Ulrich W. (Prof. Dr.)

Subjects
ddc:796
Abstract

Psychological stress and physical activity are interrelated, constituting a relevant association to human health, especially in children. However, the association’s nature remains elusive, i.e., why psychological stress predicts both decreased and increased physical activity. To test whether effects vary as a function of the level of analyses, we derived intensive longitudinal data via accelerometers and stress questionnaires from 74 children across 7 days as they went about their daily routines (\(\it n\) = 513 assessments). Multilevel modelling analyses revealed that between children, higher psychological stress predicted decreased physical activity (standardized beta coefficient = −0.14; \(\it p\) = 0.046). Concurrently, within those children, higher psychological stress predicted increased physical activity across days (standardized beta coefficient = 0.09; \(\it p\) = 0.015). Translated to practice, children who experienced more stress than others moved less, but children were more active on days when they experienced heightened stress. This suggests that the analyses level is crucial to the understanding of the association between psychological stress and physical activity and should be considered to receive unequivocal results. If replicated, e.g., including high-frequency sampling and experimental manipulation in everyday life for in-depth insights on underlying mechanisms and causality, our findings may be translated to individually tailored (digital) prevention and intervention strategies which target children's distress-feelings despite impairing their heightened physical activity in stressful situations and identify tipping points of chronic stress phases. Therefore, we especially call for more intensive longitudinal data approaches to tackle thus far neglected within-subject issues in the field of physical activity, sport and exercise research.

Published
2022

38. Additional file 2 of Assessing road criticality and loss of healthcare accessibility during floods: the case of Cyclone Idai, Mozambique 2019

Author

Petricola, Sami, Reinmuth, Marcel, Lautenbach, Sven, Hatfield, Charles, and Zipf, Alexander

Abstract

Additional file 2: Fig. S3. Accessibility workflow: raster- and network-based methods. Fig. S4. Accessibility to health facility, raster- vs. network-based method comparison A. Under six hours walking time to access healthcare estimations between raster- and network-based methods and B. mapped infrastructures. Fig. S5 Accessibility to health facilities in urban context, raster- vs. network-based method comparison A. population estimation B. extent of under 6 walking hours access to health facilities. Table S1. Comparison of mapped health facilities in OSM with field observation: facilities inventory, localisation comparison and damages inventory.

Published
2022
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40. Das Potenzial von Citizen Science für die Kartierung von Landschaftsveränderungen in arktischen Permafrostregionen

Author

Fritz, Oliver, Marx, Sabrina, Herfort, Benjamin, Kaiser, Soraya, Langer, Moritz, Lenz, Josefine, Thiel, Christian, Zipf, Alexander, and Earth and Climate

Subjects
intrinsic quality assessment, crowdsourcing, Citizen science, mapping, permafrost
Abstract

Monitoring permafrost thaw in the Arctic is essential for assessing global climate change impact. Citizen science approaches can make a crucial contribution to this. In a case study using a micro-mapping methodology, visitors of an exhibition mapped polygonal soil patterns in satellite images of the Arctic. The evaluation of the collected data reveals that mapping such patterns poses a bigger challenge than more established tasks, such as building detection. A simplification of the task using a binary detection approach increases the agreement in permafrost mapping. Citizen science shows great potential for permafrost research, although methods must be further tested.

Published
2022

41. Additional file 1 of Assessing road criticality and loss of healthcare accessibility during floods: the case of Cyclone Idai, Mozambique 2019

Author

Petricola, Sami, Reinmuth, Marcel, Lautenbach, Sven, Hatfield, Charles, and Zipf, Alexander

Abstract

Additional file 1: Data availability and completeness. Fig. S1. Indicator comparing population building densities from Ohsome Quality Analyst to evaluate the completeness of OSM data. Fig. S2. Comparison of mapped health facilities in OSM with field observation: facilities inventory, localisation comparison and damages inventory.

Published
2022
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43. Private Vehicles Greenhouse Gas Emission Estimation at Street Level for Berlin Based on Open Data.

Author

Ulrich, Veit, Brückner, Josephine, Schultz, Michael, Vardag, Sanam Noreen, Ludwig, Christina, Fürle, Johannes, Zia, Mohammed, Lautenbach, Sven, and Zipf, Alexander

Subjects
GREENHOUSE gases, TRAFFIC estimation, TRAFFIC flow, TRAFFIC patterns, GREENHOUSE gas mitigation, CITY traffic, STREETS
Abstract

As one of the major greenhouse gas (GHG) emitters that has not seen significant emission reductions in the previous decades, the transportation sector requires special attention from policymakers. Policy decisions, thereby need to be supported by traffic emission assessments. Estimations of traffic emissions often rely on huge amounts of actual traffic data whose availability is limited, hampering the transferability of the estimation approaches in time and space. Here, we propose a high-resolution estimation of traffic emissions, which is based entirely on open data, such as the road network and points of interest derived from OpenStreetMap (OSM). We estimated the annual average daily GHG emissions from individual motor traffic for the OSM road network in Berlin by combining the estimated Annual Average Daily Traffic Volume (AADTV) with respective emission factors. The AADTV was calculated by simulating car trips with the open routing engine Openrouteservice, weighted by activity functions based on statistics of the German Mobility Panel. Our estimated total annual GHG emissions were 7.3 million t CO2 equivalent. The highest emissions were estimated for the motorways and major roads connecting the city center with the outskirts. The application of the approach to Berlin showed that the method could reflect the traffic pattern. As the input data is freely available, the approach can be applied to other study areas within Germany with little additional effort. [ABSTRACT FROM AUTHOR]

Published
2023
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44. A Conceptual Quality Framework for Volunteered Geographic Information

Author

Ballatore, Andrea and Zipf, Alexander

Subjects
data quality, interpretability, conceptual quality, volunteered geographic information
Abstract

The assessment of the quality of volunteered geographic information (VGI) is cornerstone to understand the fitness for purpose of datasets in many application domains. While most analyses focus on geometric and positional quality, only sporadic attention has been devoted to the interpretation of the data, i.e., the communication process through which consumers try to reconstruct the meaning of information intended by its producers. Interpretability is a notoriously ephemeral, culturally rooted, and context-dependent property of the data that concerns the conceptual quality of the vocabularies, schemas, ontologies, and documentation used to describe and annotate the geographic features of interest. To operationalize conceptual quality in VGI, we propose a multi-faceted framework that includes accuracy, granularity, completeness, consistency, compliance, and richness, proposing proxy measures for each dimension. The application of the framework is illustrated in a case study on a European sample of OpenStreetMap, focused specifically on conceptual compliance.

Published
2015

47. From Historical OpenStreetMap data to customized training samples for geospatial machine learning

Author

Wu, Zhaoyan, Li, Hao, and Zipf, Alexander

Abstract

Wu, Z, Li, H., Zipf, A. (2020). From Historical OpenStreetMap data to customized training samples for geospatial machine learning In: Minghini, M., Coetzee, S., Juhász, L., Yeboah, G., Mooney, P., Grinberger, A.Y. (Eds.). Proceedings of the Academic Track at the State of the Map 2020 Online Conference, July 4-5 2020. Available at https://zenodo.org/communities/sotm-2020

Published
2020
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48. Comparison of Simulated Fast and Green Routes for Cyclists and Pedestrians

Author

Ludwig, Christina, Lautenbach, Sven, Zipf, Alexander, Ludwig, Christina, Lautenbach, Sven, and Zipf, Alexander

Abstract

Routes with a high share of greenery are attractive for cyclist and pedestrians. We analyze how strongly such green routes differ from the respective fast routes using the openrouteservice. Greenness of streets was estimated based on OpenStreetMap data in combination with Sentinel-II imagery, 3d laser scan data and administrative information on trees on public ground. We assess the effect both at the level of the individual route and at the urban level for two German cities: Dresden and Heidelberg. For individual routes, we study how strongly green routes differ from the respective fast routes. In addition, we identify parts of the road network which represent important green corridors as well as unattractive parts which can or cannot be avoided at the cost of reasonable detours. In both cities, our results show the importance of urban green spaces for the provision of attractive green routes and provide new insights for urban planning by identifying unvegetated bottlenecks in the street network for which no green alternatives exist at this point.

Published
2021
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49. The sketch map tool facilitates the assessment of OpenStreetMap data for participatory mapping

Author

Klonner, Carolin, Hartmann, Maximilian, Dischl, Rebecca, Djami, Lily, Anderson, Liana, Raifer, Martin, Lima-Silva, Fernanda, Castro Degrossi, Lívia, Zipf, Alexander, Albuquerque, João Porto de, Klonner, Carolin, Hartmann, Maximilian, Dischl, Rebecca, Djami, Lily, Anderson, Liana, Raifer, Martin, Lima-Silva, Fernanda, Castro Degrossi, Lívia, Zipf, Alexander, and Albuquerque, João Porto de

Abstract

A worldwide increase in the number of people and areas affected by disasters has led to more and more approaches that focus on the integration of local knowledge into disaster risk reduction processes. The research at hand shows a method for formalizing this local knowledge via sketch maps in the context of flooding. The Sketch Map Tool enables not only the visualization of this local knowledge and analyses of OpenStreetMap data quality but also the communication of the results of these analyses in an understandable way. Since the tool will be open-source and several analyses are made automatically, the tool also offers a method for local governments in areas where historic data or financial means for flood mitigation are limited. Example analyses for two cities in Brazil show the functionalities of the tool and allow the evaluation of its applicability. Results depict that the fitness-for-purpose analysis of the OpenStreetMap data reveals promising results to identify whether the sketch map approach can be used in a certain area or if citizens might have problems with marking their flood experiences. In this way, an intrinsic quality analysis is incorporated into a participatory mapping approach. Additionally, different paper formats offered for printing enable not only individual mapping but also group mapping. Future work will focus on advancing the automation of all steps of the tool to allow members of local governments without specific technical knowledge to apply the Sketch Map Tool for their own study areas.

Published
2021

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