Geographic features recognition for heritage landscape mapping – Case study: The Banda Islands, Maluku, Indonesia

This study examines methods of geographic features recognition from historic maps using CNN and OBIA. These two methods are compared to reveal which one is most suitable to be applied to the historic maps dataset of the Banda Islands, Indonesia. The characteristics of cartographic images become the main challenge in this study. The geographic features are divided into buildings, coastline, and fortress. The results show that CNN is superior to OBIA in terms of statistical performance. Buildings and coastline give excellent results for CNN analysis, while fortress is harder to be interpreted by the model. On the other hand, OBIA reveals a very satisfying result is very depending on the maps’ scales. In the aspect of technical procedure, OBIA offers easier steps in pre-processing, in-process and post-processing/finalisation which can be an advantage for a wide range of users over CNN

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

Assessing contextual descriptive features for plot-based classification of urban areas

A methodology for mapping urban land-use types integrating information from multiple data sources (high spatial resolution imagery, LiDAR data, and cadastral plots) is presented. A large set of complementary descriptive features that allow distinguishing different urban structures (historical, urban, residential, and industrial) is extracted and, after a selection process, a plot-based image classification approach applied, facilitating to directly relate the classification results and the urban descriptive parameters computed to the existent land-use/land-cover units in geospatial databases. The descriptive features are extracted by considering different hierarchical scale levels with semantic meaning in urban environments: buildings, plots, and urban blocks. Plots are characterised by means of image-based (spectral and textural), three-dimensional, and geometrical features. In addition, two groups of contextual features are defined: internal and external. Internal contextual features describe the main land cover types inside the plot (buildings and vegetation). External contextual features describe each object in terms of the properties of the urban block to which it belongs. After the evaluation in an heterogeneous Mediterranean urban area, the land-use classification accuracy values obtained show that the complementary descriptive features proposed improve the characterisation of urban typologies. A progressive introduction of the different groups of descriptive features in the classification tests show how the subsequent addition of internal and external contextual features have a positive effect by increasing the final accuracy of the urban classes considered in this study. © 2012 Elsevier B.V.The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation and FEDER in the framework of the projects CGL2009-14220 and CGL2010-19591/BTE, and the support of the Spanish Instituto Geografico Nacional (IGN).Hermosilla, T.; Ruiz Fernández, LÁ.; Recio Recio, JA.; Cambra López, M. (2012). Assessing contextual descriptive features for plot-based classification of urban areas. Landscape and Urban Planning. 106(1):124-137. doi:10.1016/j.landurbplan.2012.02.008S124137106

Development of remote sensing technology in New Zealand, part 1. Seismotectonic, structural, volcanologic and geomorphic study of New Zealand, part 2. Indigenous forest assessment, part 3. Mapping land use and environmental studies in New Zealand, part 4. New Zealand forest service LANDSAT projects, part 5. Vegetation map and landform map of Aupouri Peninsula, Northland, part 6. Geographical applications of LANDSAT mapping, part 7

The author has identified the following significant results. Inspection of pixels obtained from LANDSAT of New Zealand revealed that not only can ships and their wakes be detected, but that information on the size, state of motion, and direction of movement was inferred by calculating the total number of pixels occupied by the vessel and wake, the orientation of these pixels, and the sum of their radiance values above the background level. Computer enhanced images showing the Waimihia State Forest and much of Kaingaroa State Forest on 22 December 1975 were examined. Most major forest categories were distinguished on LANDSAT imagery. However, the LANDSAT imagery seemed to be most useful for updating and checking existing forest maps, rather than making new maps with many forest categories. Snow studies were performed using two basins: Six Mile Creek and Mt. Robert. The differences in radiance levels indicated that a greater areal snow cover in Six Mile Creek Basin with the effect of lower radiance values from vegetation/snow regions. A comparison of the two visible bands (MSS 4 and 5) demonstrate this difference for the two basins

Uncertainty in historical land-use reconstructions with topographic maps

The paper presents the outcomes of the uncertainty investigation of a long-term forest cover change analysis in the Polish Carpathians (nearly 20,000 km2) and Swiss Alps (nearly 10,000 km2) based on topographic maps. Following Leyk et al. (2005) all possible uncertainties are grouped into three domains - production-oriented, transformation- oriented and application-oriented. We show typical examples for each uncertainty domain, encountered during the forest cover change analysis and discuss consequences for change detection. Finally, a proposal for reliability assessment is presented

Land information systems : an overview and outline of software requirements

This thesis looks at some aspects of land information systems. The introduction gives the rationale for this study, and the second chapter outlines the development of land information systems with particular reference to the cadastre. In the third chapter the software requirements for the development of land information systems are considered. Programming language and databases are discussed. The fouth chapter deals with the organisation and hardware needed for a land information system. Finally, in the fifth chapter some of the algorithms used in land information systems are presented. Four appendices cover the programmes which were developed in the course of this study, the software specification for an operational system, an example of LIS-related data in a large organisation, and the syntax of Modula-2, the programming language used for the examples

Automatic building detection and land-use classification in urban areas using multispectral high-spatial resolution imagery and LiDAR data

Urban areas areimportant environments, accounting for approximately half the population of theworld. Cities attract residents partly because they offer ample opportunitiesfor development, which often results in urban sprawl and its complex environmentalimplications. It is therefore necessary to develop technologies andmethodologies that permit monitoring the effects of various problems that havebeen or are thought to be associated with urban sprawl. These technologieswould facilitate the adoption of policies seeking to minimize the negativeeffects of urban sprawl. Solutions require a precise knowledge of the urbanenvironment under consideration to enable the development of more efficienturban zoning plans. The high dynamism of urban areas produces seeminglycontinuous alterations of land cover and use; consequently, cartographicinformation becomes quickly and is oftentimes outdated. Hence, the availabilityof detailed and up-to-date cartographic and geographic information is imperativefor an adequate management and planning of urban areas. Usually the process ofcreating land-use/land-cover maps of urban areas involves field visits andclassical photo-interpretation techniques employing aerial imagery. Thesemethodologies are expensive, time consuming, and also subjective. Digital imageprocessing techniques help reduce the volume of information that needs to bemanually interpreted. The aim of thisstudy is to establish a methodology to automatically detect buildings and toautomatically classify land use in urban environments using multispectralhigh-spatial resolution imagery and LiDAR data. These data were acquired in theframework of the Spanish National Plan for Airborne Orthophotographs, having beenavailable for public Spanish administrations. Two mainapproaches for automatic building detection and localization using high spatialresolution imagery and LiDAR data are evaluated The thresholding-based approachis founded on the establishment of two threshold values: one is the minimumheight to be considered as a building, defined using the LiDAR data; the other isthe presence of vegetation, defined with the spectral response. The otherapproach follows the standard scheme of object-based image classification:segmentation, feature extraction and selection, and classification, hereperformed using decision trees. In addition, the effect of including contextualrelations with shadows in the building detection process is evaluated. Qualityassessment is performed at both area and object levels. Area-level assessments evaluatethe building delineation performance whereas object-level assessments evaluatethe accuracy in the spatial location of individual buildings. Urban land-useclassification is achieved by applying object-based image analysis techniques. Objects are defined using the boundaries of cadastral plots. The plots were characterizedto achieve the classification by employing a descriptive feature setspecifically designed to describe urban environments. The proposed descriptivefeatures aim to emulate human cognition by numerically quantifying theproperties of the image elements and so enable each to be distinguishable. These features describe each plot as a single entity based on several aspectsthat reflect the information used: spectral, three-dimensional, and geometrictypologies. In addition, a set of contextual features at both the internal andexternal levels is defined. Internal context features describe an object withrespect to the land cover types contained within the plots, which were, in thiscase, buildings and vegetation. External context features characterise eachobject by considering the common properties of adjacent objects that, whencombined, create an aggregate in a higher level than plot level: urban blocks. Results show that thresholding-based building detection approachperforms better in the different scenarios assessed. This method produces amore accurate building delineation and object detection than the object-basedclassification method. The building type appears as a key factor in thebuilding detection performance. Thus, urban and industrial areas show betteraccuracies in detection metrics than suburban areas, due to the small size ofsuburban constructions, combined with the prominent presence of trees insuburban classes, hindering the building detection process. The relationsbetween buildings and shadows improve the object-level detection, removingsmall objects erroneously detected as buildings that negatively affect to thequality indices. Classificationtest results show that internal and external context features complement theimage-derived features, improving the classification accuracy values of urbanclasses, especially between classes that show similarities in their image-basedand three-dimensional features. Context features enable a superiordiscrimination of suburban building typologies, of planned urban areas andhistorical areas, and also of planned urban areas and isolated buildings. The outcomes showthat these automatic methodologies are especially suitable for computing usefulinformation for constructing and updating land-use/land-cover geospatialdatabases. Digital image processing-based methodologies provide better resultsthan visual interpretation-based methods. Thus, automatic building detectiontechniques produce a superior estimation of built-up surface in an objectivemanner, independent of human operators. The combination of building detectionand automatic classification of land use in urban areas enable the distinguishingand describing of different urban typologies, contributing to greater accuracyand information than standard visual interpretation-based techniques. Theproposed methodology, based on an automated descriptive feature extraction fromLiDAR images and data, is appropriate for city mapping, urban landscapecharacterisation and management, and the updating of geospatial databases, allof which provide novel tools to increase the frequency and efficiency of thestudy of complex urban areas