Large-Scale Mapping of Human Activity using Geo-Tagged Videos

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analysis framework, termed hidden two-stream networks, to recognize a range of activities in YouTube videos. This framework is efficient and can run in real time or faster which is important for recognizing events as they occur in streaming video or for reducing latency in analyzing already captured video. This is, in turn, important for using video in smart-city applications. We perform a series of experiments to show our approach is able to accurately map activities both spatially and temporally. We also demonstrate the advantages of using the visual content over the tags/titles.Comment: Accepted at ACM SIGSPATIAL 201

Mining Spatio-Temporal Datasets: Relevance, Challenges and Current Research Directions

Spatio-temporal data usually records the states over time of an object, an event or a position in space. Spatio-temporal data can be found in several application fields, such as traffic management, environment monitoring, weather forerast, etc. In the past, huge effort was devoted to spatial data representation and manipulation with particular focus on its visualisation. More recently, the interest of many users has shifted from static views of geospatial phenomena, which capture its “spatiality” only, to more advanced means of discovering dynamic relationships among the patterns and events contained in the data as well as understanding the changes occurring in spatial data over time

High-resolution SAR images for fire susceptibility estimation in urban forestry

We present an adaptive system for the automatic assessment of both physical and anthropic fire impact factors on periurban forestries. The aim is to provide an integrated methodology exploiting a complex data structure built upon a multi resolution grid gathering historical land exploitation and meteorological data, records of human habits together with suitably segmented and interpreted high resolution X-SAR images, and several other information sources. The contribution of the model and its novelty rely mainly on the definition of a learning schema lifting different factors and aspects of fire causes, including physical, social and behavioural ones, to the design of a fire susceptibility map, of a specific urban forestry. The outcome is an integrated geospatial database providing an infrastructure that merges cartography, heterogeneous data and complex analysis, in so establishing a digital environment where users and tools are interactively connected in an efficient and flexible way

Marine Data Fusion for Analyzing Spatio-Temporal Ocean Region Connectivity

This thesis develops methods to automate and objectify the connectivity analysis between ocean regions. Existing methods for connectivity analysis often rely on manual integration of expert knowledge, which renders the processing of large amounts of data tedious. This thesis presents a new framework for Data Fusion that provides several approaches for automation and objectification of the entire analysis process. It identifies different complexities of connectivity analysis and shows how the Data Fusion framework can be applied and adapted to them. The framework is used in this thesis to analyze geo-referenced trajectories of fish larvae in the western Mediterranean Sea, to trace the spreading pathways of newly formed water in the subpolar North Atlantic based on their hydrographic properties, and to gauge their temporal change. These examples introduce a new, and highly relevant field of application for the established Data Science methods that were used and innovatively combined in the framework. New directions for further development of these methods are opened up which go beyond optimization of existing methods. The Marine Science, more precisely Physical Oceanography, benefits from the new possibilities to analyze large amounts of data quickly and objectively for its exact research questions. This thesis is a foray into the new field of Marine Data Science. It practically and theoretically explores the possibilities of combining Data Science and the Marine Sciences advantageously for both sides. The example of automating and objectifying connectivity analysis between marine regions in this thesis shows the added value of combining Data Science and Marine Science. This thesis also presents initial insights and ideas on how researchers from both disciplines can position themselves to thrive as Marine Data Scientists and simultaneously advance our understanding of the ocean

Applications of Satellite Earth Observations section - NEODAAS: Providing satellite data for efficient research

The NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) provides a central point of Earth Observation (EO) satellite data access and expertise for UK researchers. The service is tailored to individual users’ requirements to ensure that researchers can focus effort on their science, rather than struggling with correct use of unfamiliar satellite data

Satellite monitoring of harmful algal blooms (HABs) to protect the aquaculture industry

Harmful algal blooms (HABs) can cause sudden and considerable losses to fish farms, for example 500,000 salmon during one bloom in Shetland, and also present a threat to human health. Early warning allows the industry to take protective measures. PML's satellite monitoring of HABs is now funded by the Scottish aquaculture industry. The service involves processing EO ocean colour data from NASA and ESA in near-real time, and applying novel techniques for discriminating certain harmful blooms from harmless algae. Within the AQUA-USERS project we are extending this capability to further HAB species within several European countries

Spatio-temporal SNN : integrating time and space in the clustering process

Dissertação de mestrado em Engenharia e Gestão de Sistemas de InformaçãoSpatio-temporal clustering is a new subfield of data mining that is increasingly gaining scientific attention due to the technical advances of location-based or environmental devices that register position, time and, in some cases, other semantic attributes. This process intends to group objects based in their spatial and temporal similarity helping to discover interesting patterns and correlations in large datasets. One of the main challenges of this area is that there are different types of spatio-temporal data and there is no general approach to treat all these types. Another challenge still unresolved is the ability to integrate several dimensions in the clustering process with a general-purpose approach. Moreover, it was also possible to verify that few works address their implementations under the SNN (Shared Nearest Neighbour) algorithm, which gives the opportunity to propose an innovative extension of this particular algorithm. This work intends to implement in the SNN clustering algorithm the ability to deal with spatio-temporal data allowing the integration of space, time and one or more semantic attributes in the clustering process. In this document, background knowledge about clustering, spatial clustering and spatio-temporal clustering are presented along with a summary of the main approaches followed to cluster spatio-temporal data with different clustering algorithms. Based on those approaches, and in the analysis of their advantages and disadvantages, the boundaries of this work are defined in order to incorporate the space, time and semantic attribute dimensions in the SNN algorithm and thus propose the 4D+SNN approach. The results presented in this work are very promising as the approach proposed is able to identify interesting patterns on spatio-temporal data. Concretely, it can identify clusters taking into account simultaneously the spatial and temporal dimension and it also has good results when adding one or more semantic attributes.O clustering espaço-temporal é uma nova área do data mining que está a ganhar crescente atenção por parte da comunidade científica devido aos avanços tecnológicos dos dispositivos de localização ou monitorização ambiental que registam posição, tempo e, em alguns casos, outros atributos semânticos. Este processo pretende agrupar objectos segundo as suas similaridades espaciais e temporais ajudando assim a descobrir padrões interessantes e correlações em grandes conjuntos de dados. Um dos grandes desafios nesta área é a existência de vários tipos de dados espaço-temporais e não existe uma abordagem geral para tratar todos estes tipos. Outro desafio ainda por resolver é a capacidade para integrar várias dimensões no processo de clustering com uma abordagem geral. Além disso, foi possível verificar que poucos trabalhos de investigação usam o algoritmo SNN (Shared Nearest Neighbour) nas suas implementações o que dá a oportunidade para propor uma extensão inovadora para este algoritmo em particular. Este trabalho pretende implementar no algoritmo de clustering SNN a capacidade para lidar com dados espaço-temporais permitindo assim a integração do espaço, tempo e um ou mais atributos semânticos no processo de clustering. Neste documento, serão apresentados alguns conceitos sobre clustering, clustering espacial e clustering espaço-temporal assim como um resumo das principais abordagens usadas para fazer o clustering de dados espaço-temporais com algoritmos de clustering diferentes. Baseado nestas abordagens e na análise das suas vantagens e desvantagens, serão definidos os limites deste trabalho de modo a incorporar as dimensões espaço, tempo e atributo semântico no algoritmo SNN e, assim, propor a abordagem 4D+SNN. Os resultados apresentados neste trabalho são bastante promissores pois a abordagem proposta é capaz de identificar padrões interessantes em dados espaço-temporais. Concretamente, consegue identificar clusters tendo em consideração simultaneamente as dimensões espaço e tempo e também obtém bons resultados quando adicionando um ou mais atributos semânticos