Terrain-Informed Self-Supervised Learning: Enhancing Building Footprint Extraction from LiDAR Data with Limited Annotations

Estimating building footprint maps from geospatial data is of paramount importance in urban planning, development, disaster management, and various other applications. Deep learning methodologies have gained prominence in building segmentation maps, offering the promise of precise footprint extraction without extensive post-processing. However, these methods face challenges in generalization and label efficiency, particularly in remote sensing, where obtaining accurate labels can be both expensive and time-consuming. To address these challenges, we propose terrain-aware self-supervised learning, tailored to remote sensing, using digital elevation models from LiDAR data. We propose to learn a model to differentiate between bare Earth and superimposed structures enabling the network to implicitly learn domain-relevant features without the need for extensive pixel-level annotations. We test the effectiveness of our approach by evaluating building segmentation performance on test datasets with varying label fractions. Remarkably, with only 1% of the labels (equivalent to 25 labeled examples), our method improves over ImageNet pre-training, showing the advantage of leveraging unlabeled data for feature extraction in the domain of remote sensing. The performance improvement is more pronounced in few-shot scenarios and gradually closes the gap with ImageNet pre-training as the label fraction increases. We test on a dataset characterized by substantial distribution shifts and labeling errors to demonstrate the generalizability of our approach. When compared to other baselines, including ImageNet pretraining and more complex architectures, our approach consistently performs better, demonstrating the efficiency and effectiveness of self-supervised terrain-aware feature learning

Introduction to Big Data Computing for Geospatial Applications

The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates the opportunities for using big data for geospatial applications. Crucial to the advancements highlighted here is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms. This editorial first introduces the background and motivation of this special issue followed by an overview of the ten included articles. Conclusion and future research directions are provided in the last section

Transportation data InTegration and ANalytic

State transportation agencies regularly collect and store various types of data for different uses such as planning, traffic operations, design, and construction. These large datasets contain treasure troves of information that could be fused and mined, but the size and complexity of data mining require the use of advanced tools such as big data analytics, machine learning, and cluster computing. TITAN (Transportation data InTegration and ANalytics) is an initial prototype of an interactive web-based platform that demonstrates the possibilities of such big data software. The current study succeeded in showing a user-friendly front end, graphical in nature, and a scalable back end capable of integrating multiple big databases with minimal latencies. This thesis documents how the key components of TITAN were designed. Several applications, including mobility, safety, transit performance, and predictive crash analytics, are used to explore the strengths and limitations of the platform. A comparative analysis of the current TITAN platform with traditional database systems such as Oracle and Tableau is also conducted to explain who needs to use the platform and when to use which platform. As TITAN was shown to be feasible and efficient, the future research direction should aim to add more types of data and deploy TITAN in various data-driven decision-making processes.Includes bibliographical reference

StudMap 3.0 : an interoperable web-based platform for geospatial data offers in academic life

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesGeographic Information Systems has now entered the realm of web and yields for feasible solutions to balance the technology offers with the users’ needs to share, access and explore the massive amounts of geodata available. Challenges occur when moving forward from old 2D platforms towards innovative and integrated webGIS systems that align functionality with the necessity to grant a complete understanding of the surrounding reality. 3D space responds to this but, however, stands only at the beginning of its era and cannot yet reach the development of 2D web integration. Research is now aiming at possible webGIS solutions to adapt to the special structure imposed by 3D data. In this context, this thesis focuses on designing an architecture for 2D and 3D geospatial data integration on a student-oriented web platform. This concept was further delivered and validated through a real case scenario – Studmap 3.0, a webGIS platform to serve the students of the University of Muenster in their academical life. The portal currently grants availability of geospatial data and web services of regional interest in a smart GIS environment that allows access and comparison of official services with own data. The implementation of Studmap 3.0 aided in the continuous improvement of the proposed architecture model and developed under a design science research cycle that reached its end once the final approval of its users was attained via a usability evaluation. Final strengths and drawbacks of the proposed architecture were ultimately identified together with an expert usability evaluation and a lab-based usability test of the resulting portal interface suitability for academic use. The results fall under the acceptable range with an 83.75 score for the System Usability Scale standardized questions when addressed to experts and a score of 83.87 when addressed to students. For the open-ended questions, the interface received an overall positive critique. A summary of future participants’ opinion on the benefits, drawbacks and proposed improvements was also delivered. Peers interested in similar concepts can use both this model and its final remarks as a reference for their work

A Data-driven, High-performance and Intelligent CyberInfrastructure to Advance Spatial Sciences

abstract: In the field of Geographic Information Science (GIScience), we have witnessed the unprecedented data deluge brought about by the rapid advancement of high-resolution data observing technologies. For example, with the advancement of Earth Observation (EO) technologies, a massive amount of EO data including remote sensing data and other sensor observation data about earthquake, climate, ocean, hydrology, volcano, glacier, etc., are being collected on a daily basis by a wide range of organizations. In addition to the observation data, human-generated data including microblogs, photos, consumption records, evaluations, unstructured webpages and other Volunteered Geographical Information (VGI) are incessantly generated and shared on the Internet. Meanwhile, the emerging cyberinfrastructure rapidly increases our capacity for handling such massive data with regard to data collection and management, data integration and interoperability, data transmission and visualization, high-performance computing, etc. Cyberinfrastructure (CI) consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high-performance networks to improve research productivity and enable breakthroughs that are not otherwise possible. The Geospatial CI (GCI, or CyberGIS), as the synthesis of CI and GIScience has inherent advantages in enabling computationally intensive spatial analysis and modeling (SAM) and collaborative geospatial problem solving and decision making. This dissertation is dedicated to addressing several critical issues and improving the performance of existing methodologies and systems in the field of CyberGIS. My dissertation will include three parts: The first part is focused on developing methodologies to help public researchers find appropriate open geo-spatial datasets from millions of records provided by thousands of organizations scattered around the world efficiently and effectively. Machine learning and semantic search methods will be utilized in this research. The second part develops an interoperable and replicable geoprocessing service by synthesizing the high-performance computing (HPC) environment, the core spatial statistic/analysis algorithms from the widely adopted open source python package – Python Spatial Analysis Library (PySAL), and rich datasets acquired from the first research. The third part is dedicated to studying optimization strategies for feature data transmission and visualization. This study is intended for solving the performance issue in large feature data transmission through the Internet and visualization on the client (browser) side. Taken together, the three parts constitute an endeavor towards the methodological improvement and implementation practice of the data-driven, high-performance and intelligent CI to advance spatial sciences.Dissertation/ThesisDoctoral Dissertation Geography 201

Filtering of LiDAR point cloud with respect to creating precise DEM : a performance analysis of two selected point cloud SW-packages

Reguleringsplanlegging av modellbaserte veiprosjekter krever digitale terrengmodeller med god høydenøyaktighet. Slike modeller opprettes fra bakkepunkter i punktskyer fra laserskanning, som beskriver både terrengoverflate og objekter i et aktuelt område. Det er derfor nødvendig med gode filtreringsalgoritmer for klassifisering av terrengoverflaten. Formålet til dette forsøket er å undersøke egnetheten punktskybehandlingssystemene ALDPAT og QTM har til filtrering av LiDAR-punktskyer. Denne filtreringen er gjort med hensyn til opprettelse av digitale terrengmodeller til reguleringsplanleggingsfasen for modellbaserte veiprosjekter etter håndbok V770 Modellgrunnlag fra Vegdirektoratet. Grenstøl i Tvedestrand kommune, innenfor avgrensningen til veiprosjektet E18 Tvedestrand – Arendal, som stod ferdig i 2019 er valgt som forsøksområde. Fem testområder som representerer terreng og vegetasjonsvariasjonene i det aktuelle området er valgt. Tre av dem er videre klassifisert som vegetasjon og to som harde flater. Datasettene som benyttes stammer fra laserskanning fra helikopter utført i forbindelse med oppstart for utbyggingen av prosjektet i 2016. Disse består av en punktsky per testområde, og en DTM for hele området, som er hentet fra forvaltningsløsningen høydedata.no. DTM er brukt som sammenligningsgrunnlag, mens punktskyene er filtrert med fire ulike filtreringsalgoritmer i ALDPAT, og med en filtreringsalgoritme i QTM. Videre er det foretatt geometrisk kontroll av høydeavvikene mellom DTM-er opprettet fra de filtrerte punktskyene og sammenligningsgrunnlaget. Resultatene fra den geometriske kontrollen viser god evne til filtrering for begge punktskybehandlingssystemene. For alle filtrene er det i enkelte testområder høy prosentandel grove feil. Ett av de to harde testområdene ble som følge av dette utelukket. For det gjenværende harde testområdet presterer QTM aller best. QTM virker også å prestere svært godt i de vegeterte områdene, men ulike filtreringsalgoritmer i ALDPAT viser antydninger til å egne seg bedre. Med bakgrunn i benyttede datasett, testområder og resultater fra geometrisk kontroll av høydeavvik er det indikasjoner til at QTM er det totalt sett best egnede punktskybehandlingssystemet til formålet.Zoning planning for model-based road projects are dependent on good digital elevation models with high absolute height accuracy. Digital elevation models are created from ground points in point clouds derived from laser scanning. These contain many points that describe both the terrain surface and objects. Good filtering algorithms for classification of the terrain surface in point clouds are therefore necessary. The target of this survey is to examine the filtering suitability for the point cloud software packages ALDPAT and QTM. The filtering is done with respect to creating digital elevation models used for zoning planning for model-based road projects according to handbook V770 Modellgrunnlag from The Norwegian Public Roads Administration.M-GEO

A general-purpose framework for parallel processing of large-scale LiDAR data

Light detection and ranging (LiDAR) data are essential for scientific discoveries such as Earth and ecological sciences, environmental applications, and responding to natural disasters. While collecting LiDAR data over large areas is quite possible the subsequent processing steps typically involve large computational demands. Efficiently storing, managing, and processing LiDAR data are the prerequisite steps for enabling these LiDAR-based applications. However, handling LiDAR data poses grand geoprocessing challenges due to data and computational intensity. To tackle such challenges, we developed a general-purpose scalable framework coupled with a sophisticated data decomposition and parallelization strategy to efficiently handle ‘big’ LiDAR data collections. The contributions of this research were (1) a tile-based spatial index to manage big LiDAR data in the scalable and fault-tolerable Hadoop distributed file system, (2) two spatial decomposition techniques to enable efficient parallelization of different types of LiDAR processing tasks, and (3) by coupling existing LiDAR processing tools with Hadoop, a variety of LiDAR data processing tasks can be conducted in parallel in a highly scalable distributed computing environment using an online geoprocessing application. A proof-of-concept prototype is presented here to demonstrate the feasibility, performance, and scalability of the proposed framework