A Neural Network Approach to Flood Mapping Using Satellite Imagery

This paper presents a new approach to flood mapping using satellite synthetic-aperture radar (SAR) images that is based on intelligent techniques. In particular, we apply artificial neural networks, self-organizing Kohonen's maps (SOMs), for SAR image segmentation and classification. Our approach was used to process data from different satellite SAR instruments (ERS-2/SAR, ENVISAT/ASAR, RADARSAT-1) for different flood events: the Tisza river, Ukraine and Hungary, 2001; the Huaihe river, China, 2007; the Mekong river, Thailand and Laos, 2008; and the Koshi river, India and Nepal, 2008

Overlap Removal of Dimensionality Reduction Scatterplot Layouts

Dimensionality Reduction (DR) scatterplot layouts have become a ubiquitous visualization tool for analyzing multidimensional data items with presence in different areas. Despite its popularity, scatterplots suffer from occlusion, especially when markers convey information, making it troublesome for users to estimate items' groups' sizes and, more importantly, potentially obfuscating critical items for the analysis under execution. Different strategies have been devised to address this issue, either producing overlap-free layouts, lacking the powerful capabilities of contemporary DR techniques in uncover interesting data patterns, or eliminating overlaps as a post-processing strategy. Despite the good results of post-processing techniques, the best methods typically expand or distort the scatterplot area, thus reducing markers' size (sometimes) to unreadable dimensions, defeating the purpose of removing overlaps. This paper presents a novel post-processing strategy to remove DR layouts' overlaps that faithfully preserves the original layout's characteristics and markers' sizes. We show that the proposed strategy surpasses the state-of-the-art in overlap removal through an extensive comparative evaluation considering multiple different metrics while it is 2 or 3 orders of magnitude faster for large datasets.Comment: 11 pages and 9 figure

Market Segmentation Analysis and Visualization Using K-Mode Clustering Algorithm for E-Commerce Business

Today all business organizations are adopting data driven strategies to generate more revenue out of their business. Growing startups are investing a lot of money in data economy to maximize profits of business organizations by developing intelligent tools backed by machine learning and artificial intelligence. The nature of BI tool depends on factor like business goals, size, model, technology etc. In this paper architecture of business intelligence tool and decision process has been discussed with a focus on market segmentation, based on user behavior analysis using k-mode clustering algorithm and user geographical distributions. The proposed toolkit also incorporates interactive visualizations and maps

Rooftop-place suitability analysis for urban air mobility Hubs: A GIS and neural network approach

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesNowadays, constant overpopulation and urban expansion in cities worldwide have led to several transport-related challenges. Traffic congestion, long commuting, parking difficulties, automobile dependence, high infrastructure maintenance costs, poor public transportation, and loss of public space are some of the problems that afflict major metropolitan areas. Trying to provide a solution for the future inner-city transportation, several companies have worked in recent years to design aircraft prototypes that base their technology on current UAVs. Therefore, vehicles with electrical Vertical Take-Off and Landing (eVTOL) technology are rapidly emerging so that they can be included in the Urban Air Mobility (UAM) system. For this to become a reality, space agencies, governments and academics are generating concepts and recommendations to be considered a safe means of transportation for citizens. However, one of the most relevant points for this future implementation is the suitable location of the potential UAM hubs within the metropolitan areas. Since although UAM vehicles can take advantage of infrastructure such as roofs of buildings to clear and land, several criteria must be considered to find the ideal location. As a solution, this thesis seeks to carry out an integral rooftop-place suitability analysis by involving both the essential variables of the urban ecosystem and the adequate rooftop surfaces for UAM operability. The study area selected for this research is Manhattan (New York, U.S), which is the most densely populated metropolitan area of one of the megacities in the world. The applied methodology has an unsupervised-data-driving and GIS-based approach, which is covered in three sections. The first part is responsible for analyzing the suitability of place when evaluating spatial patterns given by the application of Self-Organizing Maps on the urban ecosystem variables attached to the city census blocks. The second part is based on the development of an algorithm in Python for both the evaluation of the flatness of the roof surfaces and the definition of the UAM platform type suitable for its settlement. The final stage performs a combined analysis of the suitability indexes generated for the development of UAM hubs. Results reflect that 16% of the roofs in the study area have high integral suitability for the development of UAM hubs, where UAVs platforms and Vertistops (small size platforms) are the types that can be the most settled in Manhattan. The reproducibility self-assessment of this research when considering Nüst et al. [45] criteria (https://osf.io/j97zp/) is: 2, 1, 2, 1, 1 (input data, preprocessing, methods, computational environment, results). GitHub repository code is available in https://github.com/carlosjdelgadonovaims/rooftop-place_suitability_analysis_for_Urban_Air_Mobility_hub

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

Automatic Image Segmentation Optimized by Bilateral Filtering

The object-based methodology is one of the most commonly used strategies for processing high spatial resolution images. A prerequisite to object-based image analysis is image segmentation, which is normally defined as the subdivision of an image into separated regions. This study proposes a new image segmentation methodology based on a self-calibrating multi-band region growing approach. Two multispectral aerial images were used in this study. The unsupervised image segmentation approach begins with a first step based on a bidirectional filtering, in order to eliminate noise, smooth the initial image and preserve edges. The results are compared with ones obtained from Definiens Developper software