User generated spatial content sources for land use/land cover validation purposes : suitability analysis and integration model

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsTraditional geographic information has been produced by mapping agencies and corporations, using high skilled people as well as expensive precision equipment and procedures, in a very costly approach. The production of land use and land cover databases are just one example of such traditional approach. On the other side, The amount of Geographic Information created and shared by citizens through the Web has been increasing exponentially during the last decade, resulting from the emergence and popularization of technologies such as the Web 2.0, cloud computing, GPS, smart phones, among others. Such comprehensive amount of free geographic data might have valuable information to extract and thus opening great possibilities to improve significantly the production of land use and land cover databases. In this thesis we explored the feasibility of using geographic data from different user generated spatial content initiatives in the process of land use and land cover database production. Data from Panoramio, Flickr and OpenStreetMap were explored in terms of their spatial and temporal distribution, and their distribution over the different land use and land cover classes. We then proposed a conceptual model to integrate data from suitable user generated spatial content initiatives based on identified dissimilarities among a comprehensive list of initiatives. Finally we developed a prototype implementing the proposed integration model, which was then validated by using the prototype to solve four identified use cases. We concluded that data from user generated spatial content initiatives has great value but should be integrated to increase their potential. The possibility of integrating data from such initiatives in an integration model was proved. Using the developed prototype, the relevance of the integration model was also demonstrated for different use cases

Exploratory analysis of OpenStreetMap for land use classification

In the last years, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information. This huge amount of data might be hiding a vast geographical richness and therefore research needs to be conducted to explore their potential and use it in the solution of real world problems. In this study we conduct an exploratory analysis of data from the OpenStreetMap initiative. Using the Corine Land Cover database as reference and continental Portugal as the study area, we establish a possible correspondence between both classification nomenclatures, evaluate the quality of OpenStreetMap polygon features classification against Corine Land Cover classes from level 1 nomenclature, and analyze the spatial distribution of OpenStreetMap classes over continental Portugal. A global classification accuracy around 76% and interesting coverage areas’ values are remarkable and promising results that encourages us for future research on this topic

Comparative study of Land Use/Cover classification using Flickr photos, satellite imagery and Corine land cover database

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Volunteered Geographic Information has been increasing exponentially over the last years, capturing the attention of the scientific community. Researchers have been very active exploring a vast amount of initiatives and trying to develop methodologies and possible real applications for this new source of geographic information. Land Use/Cover production is one of the areas where this type of geographic information might be very useful. In this paper we evaluate if geo-referenced and publicly available photos from the Flickr initiative can be used as a source of geographic information to help Land Use/Cover classification. Using the Corine Land Cover nomenclature, we compare the classification obtained for selected photo locations, against the classification obtained from high resolution satellite imagery for the same locations. We conclude that this source cannot be used alone for the purpose of Land Use/Cover classification but we also believe that it might contain helpful information if combined with other sources

Cyclist Route Assessment Using Machine Learning

Increasing the number of bike commutes can provide numerous benefits for individuals and communities. However, several factors including the availability of cycle paths, traffic char- acteristics, and pavement quality, can either encourage or discourage the use of bicycles. To promote cycling and understand how cyclists interact with the urban environment, it is crucial to assess the quality of cyclist routes. This paper proposes a pipeline that calculates the level of safety and comfort for cyclists by examining route segments using computer vision models trained on YOLOv5 to classify pavement types, detect pavement defects and detect the presence of cycle paths. The models for pavement type and cyclist paths had good results but the pave- ment defect model will demand more training to be used. The first experiment with the pipeline did not achieve high accuracy but helped to identify the next steps

Maestro: An Extensible General-Purpose Data Gathering and Classification Platform

Researchers who want to gather and classify data on a specific topic are doomed to use several tools in a tedious process given the lack of software tools to collect data from multiple sources for posterior analysis and classification. Our study addresses these issues by designing a novel software platform named Maestro that automatically gathers, classifies, and provides specific datasets from a dynamic set of configurable components (plugins). Extensibility is Maestro’s main feature, which allows new plugins to be incrementally added by the core team or other developers without changing the source code. To evaluate this proposal and support the discussion, a simple working example with images of the former U.S. president, Donald Trump and his facial expressions is shown

Streamlining Literature Reviews Using an Automatic and Flexible Data Gathering and Classification Platform

Literature reviews are a crucial but time-consuming and complex task in scientific research. As such, interest in automating this process using machine learning techniques has increased over the last few years. In this paper, we present a method of streamlining the process of writing literature reviews by automating several aspects of the process using Maestro v2023, an automatic and flexible data gathering and classification platform. Maestro v2023 is a revamped version of the original Maestro platform, designed to be modular and configurable, allowing users in an organization to create search contexts that automatically gather and classify data for them. We analyze the work related to literature review automation and suggest how Maestro can contribute to this field, demonstrating how the system was utilized in order to streamline our own literature review process, as well aid us in formulating the abstract and extracting relevant keywords to this paper

Investigating the feasibility of geo-tagged photographs as sources of land cover input data

Geo-tagged photographs are used increasingly as a source of Volunteered Geographic Information (VGI), which could potentially be used for land use and land cover applications. The purpose of this paper is to analyze the feasibility of using this source of spatial information for three use cases related to land cover: Calibration, validation and verification. We first provide an inventory of the metadata that are collected with geo-tagged photographs and then consider what elements would be essential, desirable, or unnecessary for the aforementioned use cases. Geo-tagged photographs were then extracted from Flickr, Panoramio and Geograph for an area of London, UK, and classified based on their usefulness for land cover mapping including an analysis of the accompanying metadata. Finally, we discuss protocols for geo-tagged photographs for use of VGI in relation to land cover applications