Integration of temporal and semantic components into the Geographic Information. Part II: Methodology

The overall objective of this research project is to enrich geographic data with temporal and semantic components in order to significantly improve spatio-temporal analysis of geographic phenomena. To achieve this goal, we intend to establish and incorporate three new layers (structures) into the core of the Geographic Information by using mark-up languages as well as defining a set of methods and tools for enriching the system to make it able to retrieve and exploit such layers (semantic-temporal, geosemantic, and incremental spatio-temporal). Besides these layers, we also propose a set of models (temporal and spatial) and two semantic engines that make the most of the enriched geographic data. The roots of the project and its definition have been previously presented in Siabato & Manso-Callejo 2011. In this new position paper, we extend such work by delineating clearly the methodology and the foundations on which we will base to define the main components of this research: the spatial model, the temporal model, the semantic layers, and the semantic engines. By putting together the former paper and this new work we try to present a comprehensive description of the whole process, from pinpointing the basic problem to describing and assessing the solution. In this new article we just mention the methods and the background to describe how we intend to define the components and integrate them into the GI

A conceptual framework and a risk management approach for interoperability between geospatial datacubes

De nos jours, nous observons un intérêt grandissant pour les bases de données géospatiales multidimensionnelles. Ces bases de données sont développées pour faciliter la prise de décisions stratégiques des organisations, et plus spécifiquement lorsqu’il s’agit de données de différentes époques et de différents niveaux de granularité. Cependant, les utilisateurs peuvent avoir besoin d’utiliser plusieurs bases de données géospatiales multidimensionnelles. Ces bases de données peuvent être sémantiquement hétérogènes et caractérisées par différent degrés de pertinence par rapport au contexte d’utilisation. Résoudre les problèmes sémantiques liés à l’hétérogénéité et à la différence de pertinence d’une manière transparente aux utilisateurs a été l’objectif principal de l’interopérabilité au cours des quinze dernières années. Dans ce contexte, différentes solutions ont été proposées pour traiter l’interopérabilité. Cependant, ces solutions ont adopté une approche non systématique. De plus, aucune solution pour résoudre des problèmes sémantiques spécifiques liés à l’interopérabilité entre les bases de données géospatiales multidimensionnelles n’a été trouvée. Dans cette thèse, nous supposons qu’il est possible de définir une approche qui traite ces problèmes sémantiques pour assurer l’interopérabilité entre les bases de données géospatiales multidimensionnelles. Ainsi, nous définissons tout d’abord l’interopérabilité entre ces bases de données. Ensuite, nous définissons et classifions les problèmes d’hétérogénéité sémantique qui peuvent se produire au cours d’une telle interopérabilité de différentes bases de données géospatiales multidimensionnelles. Afin de résoudre ces problèmes d’hétérogénéité sémantique, nous proposons un cadre conceptuel qui se base sur la communication humaine. Dans ce cadre, une communication s’établit entre deux agents système représentant les bases de données géospatiales multidimensionnelles impliquées dans un processus d’interopérabilité. Cette communication vise à échanger de l’information sur le contenu de ces bases. Ensuite, dans l’intention d’aider les agents à prendre des décisions appropriées au cours du processus d’interopérabilité, nous évaluons un ensemble d’indicateurs de la qualité externe (fitness-for-use) des schémas et du contexte de production (ex., les métadonnées). Finalement, nous mettons en œuvre l’approche afin de montrer sa faisabilité.Today, we observe wide use of geospatial databases that are implemented in many forms (e.g., transactional centralized systems, distributed databases, multidimensional datacubes). Among those possibilities, the multidimensional datacube is more appropriate to support interactive analysis and to guide the organization’s strategic decisions, especially when different epochs and levels of information granularity are involved. However, one may need to use several geospatial multidimensional datacubes which may be semantically heterogeneous and having different degrees of appropriateness to the context of use. Overcoming the semantic problems related to the semantic heterogeneity and to the difference in the appropriateness to the context of use in a manner that is transparent to users has been the principal aim of interoperability for the last fifteen years. However, in spite of successful initiatives, today's solutions have evolved in a non systematic way. Moreover, no solution has been found to address specific semantic problems related to interoperability between geospatial datacubes. In this thesis, we suppose that it is possible to define an approach that addresses these semantic problems to support interoperability between geospatial datacubes. For that, we first describe interoperability between geospatial datacubes. Then, we define and categorize the semantic heterogeneity problems that may occur during the interoperability process of different geospatial datacubes. In order to resolve semantic heterogeneity between geospatial datacubes, we propose a conceptual framework that is essentially based on human communication. In this framework, software agents representing geospatial datacubes involved in the interoperability process communicate together. Such communication aims at exchanging information about the content of geospatial datacubes. Then, in order to help agents to make appropriate decisions during the interoperability process, we evaluate a set of indicators of the external quality (fitness-for-use) of geospatial datacube schemas and of production context (e.g., metadata). Finally, we implement the proposed approach to show its feasibility

Deep3DSketch+: Rapid 3D Modeling from Single Free-hand Sketches

The rapid development of AR/VR brings tremendous demands for 3D content. While the widely-used Computer-Aided Design (CAD) method requires a time-consuming and labor-intensive modeling process, sketch-based 3D modeling offers a potential solution as a natural form of computer-human interaction. However, the sparsity and ambiguity of sketches make it challenging to generate high-fidelity content reflecting creators' ideas. Precise drawing from multiple views or strategic step-by-step drawings is often required to tackle the challenge but is not friendly to novice users. In this work, we introduce a novel end-to-end approach, Deep3DSketch+, which performs 3D modeling using only a single free-hand sketch without inputting multiple sketches or view information. Specifically, we introduce a lightweight generation network for efficient inference in real-time and a structural-aware adversarial training approach with a Stroke Enhancement Module (SEM) to capture the structural information to facilitate learning of the realistic and fine-detailed shape structures for high-fidelity performance. Extensive experiments demonstrated the effectiveness of our approach with the state-of-the-art (SOTA) performance on both synthetic and real datasets

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

Geospatial information infrastructures

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Geospatial information infrastructures (GIIs) provide the technological, semantic,organizationalandlegalstructurethatallowforthediscovery,sharing,and use of geospatial information (GI). In this chapter, we introduce the overall concept and surrounding notions such as geographic information systems (GIS) and spatial datainfrastructures(SDI).WeoutlinethehistoryofGIIsintermsoftheorganizational andtechnologicaldevelopmentsaswellasthecurrentstate-of-art,andreflectonsome of the central challenges and possible future trajectories. We focus on the tension betweenincreasedneedsforstandardizationandtheever-acceleratingtechnological changes. We conclude that GIIs evolved as a strong underpinning contribution to implementation of the Digital Earth vision. In the future, these infrastructures are challengedtobecomeflexibleandrobustenoughtoabsorbandembracetechnological transformationsandtheaccompanyingsocietalandorganizationalimplications.With this contribution, we present the reader a comprehensive overview of the field and a solid basis for reflections about future developments

Grounding for a computational model of place

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Text printed 2 columns per page.Includes bibliographical references (leaves 66-70).Places are spatial locations that have been given meaning by human experience. The sense of a place is it's support for experiences and the emotional responses associated with them. This sense provides direction and focus for our daily lives. Physical maps and their electronic decedents deconstruct places into discrete data and require user interpretation to reconstruct the original sense of place. Is it possible to create maps that preserve this sense of place and successfully communicate it to the user? This thesis presents a model, and an application upon that model, that captures sense of place for translation, rather then requires the user to recreate it from disparate data. By grounding a human place-sense for machine interpretation, new presentations of space can be presented that more accurately mirror human cognitive conceptions. By using measures of semantic distance a user can observe the proximity of place not only in distance but also by context or association. Applications built upon this model can then construct representations that show places that are similar in feeling or reasonable destinations given the user's current location.(cont.) To accomplish this, the model attempts to understand place in the context a human might by using commonsense reasoning to analyze textual descriptions of place, and implicit statements of support for the role of these places in natural activity. It produces a semantic description of a place in terms of human action and emotion. Representations built upon these descriptions can offer powerful changes in the cognitive processing of space.Matthew Curtis Hockenberry.S.M

Efficient sketch-based 3D character modelling.

Sketch-based modelling (SBM) has undergone substantial research over the past two decades. In the early days, researchers aimed at developing techniques useful for modelling of architectural and mechanical models through sketching. With the advancement of technology used in designing visual effects for film, TV and games, the demand for highly realistic 3D character models has skyrocketed. To allow artists to create 3D character models quickly, researchers have proposed several techniques for efficient character modelling from sketched feature curves. Moreover several research groups have developed 3D shape databases to retrieve 3D models from sketched inputs. Unfortunately, the current state of the art in sketch-based organic modelling (3D character modelling) contains a lot of gaps and limitations. To bridge the gaps and improve the current sketch-based modelling techniques, this research aims to develop an approach allowing direct and interactive modelling of 3D characters from sketched feature curves, and also make use of 3D shape databases to guide the artist to create his / her desired models. The research involved finding a fusion between 3D shape retrieval, shape manipulation, and shape reconstruction / generation techniques backed by an extensive literature review, experimentation and results. The outcome of this research involved devising a novel and improved technique for sketch-based modelling, the creation of a software interface that allows the artist to quickly and easily create realistic 3D character models with comparatively less effort and learning. The proposed research work provides the tools to draw 3D shape primitives and manipulate them using simple gestures which leads to a better modelling experience than the existing state of the art SBM systems

Interactive sketching of urban procedural models

International audience3D modeling remains a notoriously difficult task for novices despite significant research effort to provide intuitive and automated systems. We tackle this problem by combining the strengths of two popular domains: sketch-based modeling and procedural modeling. On the one hand, sketch-based modeling exploits our ability to draw but requires detailed, unambiguous drawings to achieve complex models. On the other hand, procedural modeling automates the creation of precise and detailed geometry but requires the tedious definition and parameterization of procedural models. Our system uses a collection of simple procedural grammars, called snippets, as building blocks to turn sketches into realistic 3D models. We use a machine learning approach to solve the inverse problem of finding the procedural model that best explains a user sketch. We use non-photorealistic rendering to generate artificial data for training con-volutional neural networks capable of quickly recognizing the procedural rule intended by a sketch and estimating its parameters. We integrate our algorithm in a coarse-to-fine urban modeling system that allows users to create rich buildings by successively sketching the building mass, roof, facades, windows, and ornaments. A user study shows that by using our approach non-expert users can generate complex buildings in just a few minutes