A coastal and social vulnerability assessment to climatic hazards in Jamaica

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesCoastal areas provide habitats that are a source of natural protection, food, recreation, and livelihood. These ecosystems are designed to withstand the threat of natural hazards to protect inland areas. However, dynamic, and extreme climatic changes threaten to damage such areas, particularly in low-lying, small island states as Jamaica. With the Coastal Vulnerability Index (CVI) method, areas of coastal exposure were identified and assessed using the InVEST Model. It was found that 23% of the coastline is highly exposed to climatic hazards across 177 communities. Validation of the model outputs with the Disaster Inventory DesInventar Database revealed that there was statistical evidence to state that significantly more frequent events causing damage and loss of life or property occurred in areas the model identified as highly exposed than in the less exposed areas. The island's socio-economic conditions at the parish level were analyzed with descriptive statistics to determine that 48% of the population has at least one unmet basic need, with the South to South-East parishes comparably more vulnerable due to the population size and exposure in coastal areas. Therefore, the findings of this assessment will be useful for disaster planning and coastal conservation and may be replicated in similar countries, especially surrounding islands towards a regional assessment. The creation of a combined coastal and social vulnerability index provides a balanced view of both major concerns on the susceptibility of populated coastal regions. This index is critical to the advancement of how we can comparatively quantify these characteristics and highlight areas for holistic improvement of lives, not addressing both concerns in isolation

Analyzing the sensitivity of a flood risk assessment model towards its input data

The Small Island Developing States are characterized by an unstable economy and low-lying, densely populated cities, resulting in a high vulnerability to natural hazards. Flooding affects more people than any other hazard. To limit the consequences of these hazards, adequate risk assessments are indispensable. Satisfactory input data for these assessments are hard to acquire, especially in developing countries. Therefore, in this study, a methodology was developed and evaluated to test the sensitivity of a flood model towards its input data in order to determine a minimum set of indispensable data. In a first step, a flood damage assessment model was created for the case study of Annotto Bay, Jamaica. This model generates a damage map for the region based on the flood extent map of the 2001 inundations caused by Tropical Storm Michelle. Three damages were taken into account: building, road and crop damage. Twelve scenarios were generated, each with a different combination of input data, testing one of the three damage calculations for its sensitivity. One main conclusion was that population density, in combination with an average number of people per household, is a good parameter in determining the building damage when exact building locations are unknown. Furthermore, the importance of roads for an accurate visual result was demonstrated

ACCURACY AND MULTI-CORE PERFORMANCE OF MACHINE LEARNING ALGORITHMS FOR HANDWRITTEN CHARACTER RECOGNITION

There have been considerable developments in the quest for intelligent machines since the beginning of the cybernetics revolution and the advent of computers. In the last two decades with the onset of the internet the developments have been extensive. This quest for building intelligent machines have led into research on the working of human brain, which has in turn led to the development of pattern recognition models which take inspiration in their structure and performance from biological neural networks. Research in creating intelligent systems poses two main problems. The first one is to develop algorithms which can generalize and predict accurately based on previous examples. The second one is to make these algorithms run fast enough to be able to do real time tasks. The aim of this thesis is to study and compare the accuracy and multi-core performance of some of the best learning algorithms to the task of handwritten character recognition. Seven algorithms are compared for their accuracy on the MNIST database, and the test set accuracy (generalization) for the different algorithms are compared. The second task is to implement and compare the performance of two of the hierarchical Bayesian based cortical algorithms, Hierarchical Temporal Memory (HTM) and Hierarchical Expectation Refinement Algorithm (HERA) on multi-core architectures. The results indicate that the HTM and HERA algorithms can make use of the parallelism in multi-core architectures

Lossless compression with latent variable models

We develop a simple and elegant method for lossless compression using latent variable models, which we call `bits back with asymmetric numeral systems' (BB-ANS). The method involves interleaving encode and decode steps, and achieves an optimal rate when compressing batches of data. We demonstrate it rstly on the MNIST test set, showing that state-of-the-art lossless compression is possible using a small variational autoencoder (VAE) model. We then make use of a novel empirical insight, that fully convolutional generative models, trained on small images, are able to generalize to images of arbitrary size, and extend BB-ANS to hierarchical latent variable models, enabling state-of-the-art lossless compression of full-size colour images from the ImageNet dataset. We describe `Craystack', a modular software framework which we have developed for rapid prototyping of compression using deep generative models

GIS-based detection of terraced landscape heritage: comparative tests using regional DEMs and UAV data

The analysis of terraced heritage has implications in many different fields of study, as it is shaped itself by natural, socioeconomic, and cultural dynamics. Given that their abandonment impoverishes territories and communities and raises natural, especially hydrogeological hazards, and that their deactivation leads to a loss of cultural identity, this paper aims to study rapid mapping systems for their detection. Since a deep relation between high land division and the use of terraces for the exploitation of territories has been recognized, a first detection method is based on cadastral maps. The joint use of regional-scale digital elevation models (DEMs) and cadastral dataset polygons, based on a model that typically uses GIS analyses, identifies areas with a high probability of terracing. A second method is based on the use of new technologies for very high-scale data collection. The DEM models derived from UAV (unmanned aerial vehicle) photogrammetry, given their ability to determine the micro-topographical characterization of the terrain as well as the most expensive on-site techniques, can be considered an excellent low-cost means by which to locate terraced heritage. The proposed work includes comparative testing between methods implying GIS-based analysis of slope models. It aims to highlight the effectiveness of using both methods: regional-scale DEMs and cadastral maps to detect a high probability of terrace localization, and DEMs derived from the use of low-altitude aerial data and structure from motion (SfM) algorithms, which have greatly and effectively increased the use of aerial drone photogrammetry

A comparison of airborne and ground-based radar observations with rain gages during the CaPE experiment

The vicinity of KSC, where the primary ground truth site of the Tropical Rainfall Measuring Mission (TRMM) program is located, was the focal point of the Convection and Precipitation/Electrification (CaPE) experiment in Jul. and Aug. 1991. In addition to several specialized radars, local coverage was provided by the C-band (5 cm) radar at Patrick AFB. Point measurements of rain rate were provided by tipping bucket rain gage networks. Besides these ground-based activities, airborne radar measurements with X- and Ka-band nadir-looking radars on board an aircraft were also recorded. A unique combination data set of airborne radar observations with ground-based observations was obtained in the summer convective rain regime of central Florida. We present a comparison of these data intending a preliminary validation. A convective rain event was observed simultaneously by all three instrument types on the evening of 27 Jul. 1991. The high resolution aircraft radar was flown over convective cells with tops exceeding 10 km and observed reflectivities of 40 to 50 dBZ at 4 to 5 km altitude, while the low resolution surface radar observed 35 to 55 dBZ echoes and a rain gage indicated maximum surface rain rates exceeding 100 mm/hr. The height profile of reflectivity measured with the airborne radar show an attenuation of 6.5 dB/km (two way) for X-band, corresponding to a rainfall rate of 95 mm/hr

3-Dimensional Building Details from Aerial Photography for Internet Maps

This paper introduces the automated characterization of real estate (real property) for Internet mapping. It proposes a processing framework to achieve this task from vertical aerial photography and associated property information. A demonstration of the feasibility of an automated solution builds on test data from the Austrian City of Graz. Information is extracted from vertical aerial photography and various data products derived from that photography in the form of a true orthophoto, a dense digital surface model and digital terrain model, and a classification of land cover. Maps of cadastral property boundaries aid in defining real properties. Our goal is to develop a table for each property with descriptive numbers about the buildings, their dimensions, number of floors, number of windows, roof shapes, impervious surfaces, garages, sheds, vegetation, presence of a basement floor, and other descriptors of interest for each and every property of a city. From aerial sources, at a pixel size of 10 cm, we show that we have obtained positional accuracies in the range of a single pixel, an accuracy of areas in the 10% range, floor counts at an accuracy of 93% and window counts at 86% accuracy. We also introduce 3D point clouds of facades and their creation from vertical aerial photography, and how these point clouds can support the definition of complex facades