Geo-Information Harvesting from Social Media Data

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Geospatial Data Management Research: Progress and Future Directions

Without geospatial data management, today´s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIM/GIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D/4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geo-data science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the geo-related sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Data-streaming libraries and “in-situ” geo-computing on objects executed directly on the sensors will revolutionize geo-information science and bridge geo-computing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objects’ trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis

Discovery and integration of Web 2.0 content into geospatial information infrastructures: a use case in wild fire monitoring

Efficient environment monitoring has become a major concern for society to guarantee sustainable development. For instance, forest fire detection and analysis is important to provide early warning systems and identify impact. In this environmental context, availability of up-to-date information is very important for reducing damages caused. Environmental applications are deployed on top of GeospatialInformation Infrastructures (GIIs) to manage information pertaining to our environment. Suchinfrastructures are traditionally top-down infrastructures that do not consider user participation. This provokes a bottleneck in content publication and therefore a lack of content availability. On the contrary mainstream IT systems and in particular the emerging Web 2.0 Services allow active user participation that is becoming a massive source of dynamic geospatial resources. In this paper, we present a webservice, that implements a standard interface, offers a unique entry point for spatial data discovery, both in GII services and web 2.0 services. We introduce a prototype as proof of concept in a forest fire scenario, where we illustrate how to leverage scientific data and web 2.0 conten

A service-oriented middleware for integrated management of crowdsourced and sensor data streams in disaster management

The increasing number of sensors used in diverse applications has provided a massive number of continuous, unbounded, rapid data and requires the management of distinct protocols, interfaces and intermittent connections. As traditional sensor networks are error-prone and difficult to maintain, the study highlights the emerging role of “citizens as sensors” as a complementary data source to increase public awareness. To this end, an interoperable, reusable middleware for managing spatial, temporal, and thematic data using Sensor Web Enablement initiative services and a processing engine was designed, implemented, and deployed. The study found that its approach provided effective sensor data-stream access, publication, and filtering in dynamic scenarios such as disaster management, as well as it enables batch and stream management integration. Also, an interoperability analytics testing of a flood citizen observatory highlighted even variable data such as those provided by the crowd can be integrated with sensor data stream. Our approach, thus, offers a mean to improve near-real-time applications

Geospatial Information Research: State of the Art, Case Studies and Future Perspectives

Geospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors – members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany – have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

On the use of smartphones as novel photogrammetric water gauging instruments: Developing tools for crowdsourcing water levels

The term global climate change is omnipresent since the beginning of the last decade. Changes in the global climate are associated with an increase in heavy rainfalls that can cause nearly unpredictable flash floods. Consequently, spatio-temporally high-resolution monitoring of rivers becomes increasingly important. Water gauging stations continuously and precisely measure water levels. However, they are rather expensive in purchase and maintenance and are preferably installed at water bodies relevant for water management. Small-scale catchments remain often ungauged. In order to increase the data density of hydrometric monitoring networks and thus to improve the prediction quality of flood events, new, flexible and cost-effective water level measurement technologies are required. They should be oriented towards the accuracy requirements of conventional measurement systems and facilitate the observation of water levels at virtually any time, even at the smallest rivers. A possible solution is the development of a photogrammetric smartphone application (app) for crowdsourcing water levels, which merely requires voluntary users to take pictures of a river section to determine the water level. Today’s smartphones integrate high-resolution cameras, a variety of sensors, powerful processors, and mass storage. However, they are designed for the mass market and use low-cost hardware that cannot comply with the quality of geodetic measurement technology. In order to investigate the potential for mobile measurement applications, research was conducted on the smartphone as a photogrammetric measurement instrument as part of the doctoral project. The studies deal with the geometric stability of smartphone cameras regarding device-internal temperature changes and with the accuracy potential of rotation parameters measured with smartphone sensors. The results show a high, temperature-related variability of the interior orientation parameters, which is why the calibration of the camera should be carried out during the immediate measurement. The results of the sensor investigations show considerable inaccuracies when measuring rotation parameters, especially the compass angle (errors up to 90° were observed). The same applies to position parameters measured by global navigation satellite system (GNSS) receivers built into smartphones. According to the literature, positional accuracies of about 5 m are possible in best conditions. Otherwise, errors of several 10 m are to be expected. As a result, direct georeferencing of image measurements using current smartphone technology should be discouraged. In consideration of the results, the water gauging app Open Water Levels (OWL) was developed, whose methodological development and implementation constituted the core of the thesis project. OWL enables the flexible measurement of water levels via crowdsourcing without requiring additional equipment or being limited to specific river sections. Data acquisition and processing take place directly in the field, so that the water level information is immediately available. In practice, the user captures a short time-lapse sequence of a river bank with OWL, which is used to calculate a spatio-temporal texture that enables the detection of the water line. In order to translate the image measurement into 3D object space, a synthetic, photo-realistic image of the situation is created from existing 3D data of the river section to be investigated. Necessary approximations of the image orientation parameters are measured by smartphone sensors and GNSS. The assignment of camera image and synthetic image allows for the determination of the interior and exterior orientation parameters by means of space resection and finally the transfer of the image-measured 2D water line into the 3D object space to derive the prevalent water level in the reference system of the 3D data. In comparison with conventionally measured water levels, OWL reveals an accuracy potential of 2 cm on average, provided that synthetic image and camera image exhibit consistent image contents and that the water line can be reliably detected. In the present dissertation, related geometric and radiometric problems are comprehensively discussed. Furthermore, possible solutions, based on advancing developments in smartphone technology and image processing as well as the increasing availability of 3D reference data, are presented in the synthesis of the work. The app Open Water Levels, which is currently available as a beta version and has been tested on selected devices, provides a basis, which, with continuous further development, aims to achieve a final release for crowdsourcing water levels towards the establishment of new and the expansion of existing monitoring networks.Der Begriff des globalen Klimawandels ist seit Beginn des letzten Jahrzehnts allgegenwärtig. Die Veränderung des Weltklimas ist mit einer Zunahme von Starkregenereignissen verbunden, die nahezu unvorhersehbare Sturzfluten verursachen können. Folglich gewinnt die raumzeitlich hochaufgelöste Überwachung von Fließgewässern zunehmend an Bedeutung. Pegelmessstationen erfassen kontinuierlich und präzise Wasserstände, sind jedoch in Anschaffung und Wartung sehr teuer und werden vorzugsweise an wasserwirtschaftlich-relevanten Gewässern installiert. Kleinere Gewässer bleiben häufig unbeobachtet. Um die Datendichte hydrometrischer Messnetze zu erhöhen und somit die Vorhersagequalität von Hochwasserereignissen zu verbessern, sind neue, kostengünstige und flexibel einsetzbare Wasserstandsmesstechnologien erforderlich. Diese sollten sich an den Genauigkeitsanforderungen konventioneller Messsysteme orientieren und die Beobachtung von Wasserständen zu praktisch jedem Zeitpunkt, selbst an den kleinsten Flüssen, ermöglichen. Ein Lösungsvorschlag ist die Entwicklung einer photogrammetrischen Smartphone-Anwendung (App) zum Crowdsourcing von Wasserständen mit welcher freiwillige Nutzer lediglich Bilder eines Flussabschnitts aufnehmen müssen, um daraus den Wasserstand zu bestimmen. Heutige Smartphones integrieren hochauflösende Kameras, eine Vielzahl von Sensoren, leistungsfähige Prozessoren und Massenspeicher. Sie sind jedoch für den Massenmarkt konzipiert und verwenden kostengünstige Hardware, die nicht der Qualität geodätischer Messtechnik entsprechen kann. Um das Einsatzpotential in mobilen Messanwendungen zu eruieren, sind Untersuchungen zum Smartphone als photogrammetrisches Messinstrument im Rahmen des Promotionsprojekts durchgeführt worden. Die Studien befassen sich mit der geometrischen Stabilität von Smartphone-Kameras bezüglich geräteinterner Temperaturänderungen und mit dem Genauigkeitspotential von mit Smartphone-Sensoren gemessenen Rotationsparametern. Die Ergebnisse zeigen eine starke, temperaturbedingte Variabilität der inneren Orientierungsparameter, weshalb die Kalibrierung der Kamera zum unmittelbaren Messzeitpunkt erfolgen sollte. Die Ergebnisse der Sensoruntersuchungen zeigen große Ungenauigkeiten bei der Messung der Rotationsparameter, insbesondere des Kompasswinkels (Fehler von bis zu 90° festgestellt). Selbiges gilt auch für Positionsparameter, gemessen durch in Smartphones eingebaute Empfänger für Signale globaler Navigationssatellitensysteme (GNSS). Wie aus der Literatur zu entnehmen ist, lassen sich unter besten Bedingungen Lagegenauigkeiten von etwa 5 m erreichen. Abseits davon sind Fehler von mehreren 10 m zu erwarten. Infolgedessen ist von einer direkten Georeferenzierung von Bildmessungen mittels aktueller Smartphone-Technologie abzusehen. Unter Berücksichtigung der gewonnenen Erkenntnisse wurde die Pegel-App Open Water Levels (OWL) entwickelt, deren methodische Entwicklung und Implementierung den Kern der Arbeit bildete. OWL ermöglicht die flexible Messung von Wasserständen via Crowdsourcing, ohne dabei zusätzliche Ausrüstung zu verlangen oder auf spezifische Flussabschnitte beschränkt zu sein. Datenaufnahme und Verarbeitung erfolgen direkt im Feld, so dass die Pegelinformationen sofort verfügbar sind. Praktisch nimmt der Anwender mit OWL eine kurze Zeitraffersequenz eines Flussufers auf, die zur Berechnung einer Raum-Zeit-Textur dient und die Erkennung der Wasserlinie ermöglicht. Zur Übersetzung der Bildmessung in den 3D-Objektraum wird aus vorhandenen 3D-Daten des zu untersuchenden Flussabschnittes ein synthetisches, photorealistisches Abbild der Aufnahmesituation erstellt. Erforderliche Näherungen der Bildorientierungsparameter werden von Smartphone-Sensoren und GNSS gemessen. Die Zuordnung von Kamerabild und synthetischem Bild erlaubt die Bestimmung der inneren und äußeren Orientierungsparameter mittels räumlichen Rückwärtsschnitt. Nach Rekonstruktion der Aufnahmesituation lässt sich die im Bild gemessene 2D-Wasserlinie in den 3D-Objektraum projizieren und der vorherrschende Wasserstand im Referenzsystem der 3D-Daten ableiten. Im Soll-Ist-Vergleich mit konventionell gemessenen Pegeldaten zeigt OWL ein erreichbares Genauigkeitspotential von durchschnittlich 2 cm, insofern synthetisches und reales Kamerabild einen möglichst konsistenten Bildinhalt aufweisen und die Wasserlinie zuverlässig detektiert werden kann. In der vorliegenden Dissertation werden damit verbundene geometrische und radiometrische Probleme ausführlich diskutiert sowie Lösungsansätze, auf der Basis fortschreitender Entwicklungen von Smartphone-Technologie und Bildverarbeitung sowie der zunehmenden Verfügbarkeit von 3D-Referenzdaten, in der Synthese der Arbeit vorgestellt. Mit der gegenwärtig als Betaversion vorliegenden und auf ausgewählten Geräten getesteten App Open Water Levels wurde eine Basis geschaffen, die mit kontinuierlicher Weiterentwicklung eine finale Freigabe für das Crowdsourcing von Wasserständen und damit den Aufbau neuer und die Erweiterung bestehender Monitoring-Netzwerke anstrebt