An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given

Geographic Information Science (GIScience) and Geospatial Approaches for the Analysis of Historical Visual Sources and Cartographic Material

This book focuses on the use of GIScience in conjunction with historical visual sources to resolve past scenarios. The themes, knowledge gained and methodologies conducted might be of interest to a variety of scholars from the social science and humanities disciplines

Survey of the Ridracoli Dam: UAV – Based Photogrammetry and Traditional Topographic Techniques in the Inspection of Vertical Structures*

The inspection of strategic works such as dams is of vital importance both for their maintenance and for the safety of downstream populations. The reduced accessibility, both for uptake needs and for their strategic nature, and the large time needed for an inspection by traditional method do not facilitate the investigation of this type of structures. The new unmanned aerial vehicle (UAV) technology, equipped with high-performance cameras, allows for rapid photographic coverage of the whole dam system. Apart from the placement on the structure of a high number of markers, the correct geo-referencing and validation of the model also requires an important terrestrial topographic campaign by total station, Global Positioning System and laser scanner. Punctual, linear and surface analysis shows the high accuracy of the drone acquiring technique. The product is suitable for a detailed survey of the conservation status of the materials and the complete metric reconstruction of the dam system and the adjacent land. The present work explains firstly a UAV acquisition and then the first dense point cloud validation procedure of a concrete arch gravity dam. The Ridracoli dam is the object of the survey, located in the village of Santa Sofia in central Italy

Large-scale 3D environmental modelling and visualisation for flood hazard warning.

3D environment reconstruction has received great interest in recent years in areas such as city planning, virtual tourism and flood hazard warning. With the rapid development of computer technologies, it has become possible and necessary to develop new methodologies and techniques for real time simulation for virtual environments applications. This thesis proposes a novel dynamic simulation scheme for flood hazard warning. The work consists of three main parts: digital terrain modelling; 3D environmental reconstruction and system development; flood simulation models. The digital terrain model is constructed using real world measurement data of GIS, in terms of digital elevation data and satellite image data. An NTSP algorithm is proposed for very large data assessing, terrain modelling and visualisation. A pyramidal data arrangement structure is used for dealing with the requirements of terrain details with different resolutions. The 3D environmental reconstruction system is made up of environmental image segmentation for object identification, a new shape match method and an intelligent reconstruction system. The active contours-based multi-resolution vector-valued framework and the multi-seed region growing method are both used for extracting necessary objects from images. The shape match method is used with a template in the spatial domain for a 3D detailed small scale urban environment reconstruction. The intelligent reconstruction system is designed to recreate the whole model based on specific features of objects for large scale environment reconstruction. This study then proposes a new flood simulation scheme which is an important application of the 3D environmental reconstruction system. Two new flooding models have been developed. The first one is flood spreading model which is useful for large scale flood simulation. It consists of flooding image spatial segmentation, a water level calculation process, a standard gradient descent method for energy minimization, a flood region search and a merge process. The finite volume hydrodynamic model is built from shallow water equations which is useful for urban area flood simulation. The proposed 3D urban environment reconstruction system was tested on our simulation platform. The experiment results indicate that this method is capable of dealing with complicated and high resolution region reconstruction which is useful for many applications. When testing the 3D flood simulation system, the simulation results are very close to the real flood situation, and this method has faster speed and greater accuracy of simulating the inundation area in comparison to the conventional flood simulation model

The advantage of lidar digital terrain models in doline morphometry compared to topographic map based datasets – Aggtelek karst (Hungary) as an example

Doline morphometry has always been in the focus of karst geomorphological research. Recently, digital terrain model (DTM) based methods became widespread in the study of dolines. Today, LiDAR datasets provide high resolution DTMs, and automated doline recognition algorithms have been developed. In this paper, we test different datasets and a doline recognition algorithm using Aggtelek Karst (NE-Hungary) dolines as a case example. Three datasets are compared: “TOPO” dolines delineated by the classical outermost closed contour method using 1:10,000 scale topographic maps, “KRIG” dolines derived automatically from the DTM created by kriging interpolation from the digitized contours of the same topographic maps, and finally “LiDAR” dolines derived automatically from a DTM created from LiDAR data. First, we analyzed the sensitivity of the automatic method to the “depth limit” parameter, which is the threshold, below which closed depressions are considered as “errors” and are filled. In the actual case, given the typical doline size of the area and the resolution of the DTMs, we found that ca. 0.5 m is the optimal depth limit for the LiDAR dataset and 1 m for the KRIG dataset. The statistical distributions of the morphometrical properties were similar for all datasets (lognormal distribution for area and gamma distribution for depth), but the DTM-based methodology resulted larger dolines with respect to the classical method. The planform area (and related characteristics) showed very high correlations between the datasets. Depth values were less correlated and the lowest (moderately strong) correlations were observed between circularity values of the different datasets. Slope histograms calculated from the LiDAR data were used to cluster dolines, and these clusters differentiated dolines similarly to the classical depth-diameter ratio. Finally, we conclude that in the actual case, dolines can be morphometrically well characterized even by the classical topographic method, though finer results can be achieved for the depth and shape related parameters by using LiDAR data.Key words: doline morphometry, LiDAR, interpolation, slope histogram, sink point. Prednost lidarskega digitalnega modela reliefa za raziskavo morfometrije vrtač v primerjavi s podatkovno bazo topografskih kart − primer Agteleškega krasa (Madžarska)Morfometrija vrtač je bila vedno v središču kraških geomorfoloških raziskav. V zadnjem času so pri raziskavah vrtač postale zelo razširjene metode, ki temeljijo na digitalnem modelu reliefa (DMR). Lidarski podatki zagotavljajo visoko ločljivostne DMR-je, razviti so bili avtomatski algoritmi za prepoznavanje vrtač. V tem prispevku smo na primeru Agteleškega krasa v severovzhodni Madžarski preizkusili različne podatkovne baze in algoritme za prepoznavanje vrtač. Primerjali smo tri podatkovne baze: "TOPO" vrtače so razmejene na klasičen način z zunanjo zaprto plastnico na topografski karti v merilu 1: 10.000, "KRIG" vrtače so v istem merilu s pomočjo kriginga samodejno pridobljene iz digitaliziranih plastnic DMR, in "LiDAR" vrtače so samodejno pridobljene iz DMR, ki je ustvarjen iz lidarskih podatkov. Najprej smo analizirali občutljivost avtomatske metode parametra "mejne globine", ki predstavlja prag, pod katerim se depresijske oblike štejejo kot "napake" in so zapolnjene. V konkretnem primeru smo glede na običajno velikost vrtače in ločljivosti DMR ugotovili, da je optimalna globinska meja za LiDAR ca. 0,5 m in 1 m za KRIG. Pri vseh podatkovnih bazah so bile statistične porazdelitve morfometrijskih lastnosti (logaritemska normalna porazdelitev za prostor in gama porazdelitev za globino) podobne, vendar metodologija, ki temelji na DMR privede do rezultatov, ki kažejo na večje vrtače v primerjavi s klasično metodo. Rezultati območij vrtač (in njihovih značilnosti) so pokazali zelo visoke korelacije med podatkovnimi nizi. Pri globinah so bile korelacije manjše in najnižje zabeležene korelacije (srednje močne) so bile med podatki različnih podatkovnih bazah. Histogrami naklona, izračunani iz lidarskih podatkov, so bili uporabljeni za združevanje vrtač, in ti grozdi razlikujejo vrtače glede na klasično razmerje med globino in premerom. Na koncu smo ugotovili, da lahko v konkretnem primeru dobro določimo morfometrične lastnosti vrtač s klasičnimi topografskimi metodami. Podrobnejše rezultate o globinah in oblikah lahko dosežemo na podlagi lidarskih podatkov.Ključne besede: morfometrija vrtač, LiDAR, interpolacija, histogram naklona, ponor

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

Remote Sensing for Land Administration 2.0

The reprint “Land Administration 2.0” is an extension of the previous reprint “Remote Sensing for Land Administration”, another Special Issue in Remote Sensing. This reprint unpacks the responsible use and integration of emerging remote sensing techniques into the domain of land administration, including land registration, cadastre, land use planning, land valuation, land taxation, and land development. The title was chosen as “Land Administration 2.0” in reference to both this Special Issue being the second volume on the topic “Land Administration” and the next-generation requirements of land administration including demands for 3D, indoor, underground, real-time, high-accuracy, lower-cost, and interoperable land data and information