Leveraging Overhead Imagery for Localization, Mapping, and Understanding

Ground-level and overhead images provide complementary viewpoints of the world. This thesis proposes methods which leverage dense overhead imagery, in addition to sparsely distributed ground-level imagery, to advance traditional computer vision problems, such as ground-level image localization and fine-grained urban mapping. Our work focuses on three primary research areas: learning a joint feature representation between ground-level and overhead imagery to enable direct comparison for the task of image geolocalization, incorporating unlabeled overhead images by inferring labels from nearby ground-level images to improve image-driven mapping, and fusing ground-level imagery with overhead imagery to enhance understanding. The ultimate contribution of this thesis is a general framework for estimating geospatial functions, such as land cover or land use, which integrates visual evidence from both ground-level and overhead image viewpoints

Image-based models using crowdsourcing strategies

This paper aims to highlight the effectiveness of the collaboration between the modelling techniques that exploit the stereoscopic images of objects and the ability of the present-day technologies to generate images, both found in the web and gathered by other crowdsourcing techniques. Since nowadays the generation of models from images is a major low-cost resource, the whole strategy is aimed at obtaining benefits in the context of the documentation of Cultural Heritage (CH). Assuming that the documentation of CH is the basis of the protection and the conservation policies, the chances of finding images and using them to create 3D models is particularly effective when the assets in question are at risk in danger zones (wars or areas subject to natural disasters) or in areas that, for various reasons, are difficult to access. To demonstrate the advantage of using low-cost methods for the generation of 3D models of documentation with strategies that fall within the sphere of crowdsourcing, the case of the Vank cathedral modelling is presented. The Vank Cathedral in Isfahan in Iran is a building of the Safavid epoch (cent. XVII–XVIII) completely frescoed in the internal surfaces, where the architecture and especially the architectural decoration reach their peak. The experimental section of the paper also explores some aspects of usability of the digital output from the image-based modelling methods. The availability of orthophotos allows and facilitates the iconographic reading of the frescoes, adding to the radiometric data, there is the metric potentiality of reading the proportions and the compositions of the organisation of the frescoes. Furthermore, simplified and suitably schematised models can be even printed and can be used in a didactic environment, such as the knowledge dissemination intended by the museums and other cultural institutions

The role of geographic knowledge in sub-city level geolocation algorithms

Geolocation of microblog messages has been largely investigated in the lit- erature. Many solutions have been proposed that achieve good results at the city-level. Existing approaches are mainly data-driven (i.e., they rely on a training phase). However, the development of algorithms for geolocation at sub-city level is still an open problem also due to the absence of good training datasets. In this thesis, we investigate the role that external geographic know- ledge can play in geolocation approaches. We show how di)erent geographical data sources can be combined with a semantic layer to achieve reasonably accurate sub-city level geolocation. Moreover, we propose a knowledge-based method, called Sherloc, to accurately geolocate messages at sub-city level, by exploiting the presence in the message of toponyms possibly referring to the speci*c places in the target geographical area. Sherloc exploits the semantics associated with toponyms contained in gazetteers and embeds them into a metric space that captures the semantic distance among them. This allows toponyms to be represented as points and indexed by a spatial access method, allowing us to identify the semantically closest terms to a microblog message, that also form a cluster with respect to their spatial locations. In contrast to state-of-the-art methods, Sherloc requires no prior training, it is not limited to geolocating on a *xed spatial grid and it experimentally demonstrated its ability to infer the location at sub-city level with higher accuracy

Digital neighborhoods

With the advent of ‘big data’ there is an increased interest in using social media to describe city dynamics. This paper employs geo-located social media data to identify ‘digital neighborhoods’ – those areas in the city where social media is used more often. Starting with geo-located Twitter and Foursquare data for the New York City region in 2014, we applied spatial clustering techniques to detect significant groupings or ‘neighborhoods’ where social media use is high or low. The results show that beyond the business districts, digital neighborhoods occur in communities undergoing shifting socio-demographics. Neighborhoods that are not digitally oriented tend to have higher proportion of minorities and lower incomes, highlighting a social–economic divide in how social media is used in the city. Understanding the differences in these neighborhoods can help city planners interested in generating economic development proposals, civic engagement strategies, and urban design ideas that target these areas

Share Our Cultural Heritage (SOCH): Worldwide 3D Heritage Reconstruction and Visualization via Web and Mobile GIS

Despite being of paramount importance to humanity, tangible cultural heritage is often at risk from natural and anthropogenic threats worldwide. As a result, heritage discovery and conservation remain a huge challenge for both developed and developing countries, with heritage sites often inadequately cared for, be it due to a lack of resources, nonrecognition of the value by local people or authorities, human conflict, or some other reason. This paper presents an online geo-crowdsourcing system, termed Share Our Cultural Heritage (SOCH), which can be utilized for large-scale heritage documentation and sharing. Supported by web and mobile GIS, cultural heritage data such as textual stories, locations, and images can be acquired via portable devices. These data are georeferenced and presented to the public via web-mapping. Using photogrammetric modelling, acquired images are used to reconstruct heritage structures or artefacts into 3D digital models, which are then visualized on the SOCH web interface to enable public interaction. This end-to-end system incubates an online virtual community to encourage public engagement, raise awareness, and stimulate cultural heritage ownership. It also provides valuable resources for cultural heritage exploitation, management, education, and monitoring over time

MULTI-SOURCE 3D MODELS SUPPORTING ULTRASONIC TEST TO INVESTIGATE AN EGYPTIAN SCULPTURE OF THE ARCHAEOLOGICAL MUSEUM IN BOLOGNA

The paper presents the workflow and the results of an ultrasonic 3D investigation and a 3D survey application aimed at the assessment of the internal integrity of an ancient sculpture. The work aimed at highlighting the ability of methods devoted to the 3D geometry acquisition of small objects when applied to diagnosis performed by geophysical investigation. In particular, two methods widely applied for small objects modelling are considered and compared, the digital Photogrammetry with the Structure from Motion (SFM) technique and hand-held 3D scanners. The study concludes with the aim to enhance the final graphical representation of the tomographic results and to subject the obtained results to a quantitative analysis. The survey is applied to the Egyptian naophorous statue of Amenmes and Reshpu, which dates to the reign of Ramses II (1279-1213 BC) or later and is now preserved in the Civic Archaeological Museum in Bologna. In order to evaluate the internal persistency of fractures and visible damages, a 3D Ultrasonic Tomographic Imaging (UTI) test has been performed and a multi-sensor survey (image and range based) was conducted, in order to evaluate the locations of the source and receiver points as accurate as possible The presented test allowed to evaluate the material characteristics, its porosity and degradation state, which particularly affect the lower part of the statue. More in general, the project demonstrated how solution coming from the field of 3D modelling of Cultural Heritage allow the application of 3D ultrasonic tomography also on objects with complex shapes, in addition to the improved representation of the obtained results

Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics

The impact of micro-level people’s activities on urban macro-level indicators is a complex question that has been the subject of much interest among researchers and policymakers. Transportation preferences, consumption habits, communication patterns and other individual-level activities can significantly impact large-scale urban characteristics, such as the potential for innovation generation of the city. Conversely, large-scale urban characteristics can also constrain and determine the activities of their inhabitants. Therefore, understanding the interdependence and mutual reinforcement between micro- and macro-level factors is critical to defining effective public policies. The increasing availability of digital data sources, such as social media and mobile phones, has opened up new opportunities for the quantitative study of this interdependency. This paper aims to detect meaningful city clusters on the basis of a detailed analysis of the spatiotemporal activity patterns for each city. The study is carried out on a worldwide city dataset of spatiotemporal activity patterns obtained from geotagged social media data. Clustering features are obtained from unsupervised topic analyses of activity patterns. Our study compares state-of-the-art clustering models, selecting the model achieving a 2.7% greater Silhouette Score than the next-best model. Three well-separated city clusters are identified. Additionally, the study of the distribution of the City Innovation Index over these three city clusters shows discrimination of low performing from high performing cities relative to innovation. Low performing cities are identified in one well-separated cluster. Therefore, it is possible to correlate micro-scale individual-level activities to large-scale urban characteristics.This work would not have been accomplished without the financial support of CONICYT-PFCHA/DOCTORADO BECAS CHILE/2019-21190345. The last author received research funds from the Basque Government as the head of the Grupo de Inteligencia Computacional, Universidad del Pais Vasco, UPV/EHU, from 2007 until 2025. The current code for the grant is IT1689-22. Additionally, the author participates in Elkartek projects KK-2022/00051 and KK-2021/00070. The Spanish MCIN has also granted the author a research project under code PID2020-116346GB-I00

Three-Dimensional (3D) Modelling and Optimization for Multipurpose Analysis and Representation of Ancient Statues

The technological advances that have developed in the field of threedimensional (3D) survey and modelling allow us to digitally and accurately preserve many significant heritage assets that are at risk. With regard to museum assets, extensive digitalization projects aim at achieving multilingual digital libraries accessible to everyone. A first trend is geared to the use of 3D models for further specialized studies, acquiring and processing virtual detailed copies as close as possible to the shape and contents of the real one. On the other hand, many museums look today for more interactive and immersive exhibitions, which involve the visitors’ emotions, and this has contributed to the increase in the use of virtual reality and 3D models in museums installations. In this paper, we present two case studies that belong to these scenarios. Multisensor surveys have been applied to some archeological statues preserved in two museums for multipurpose analyses and representation: a UTI test, which required high detailed data about the geometry of the object, and a communicative application, which needed instead a high level of model optimization, poor geometry, but very good representation that was achieved through remeshing tools and normal maps

Modeling and Mapping Location-Dependent Human Appearance

Human appearance is highly variable and depends on individual preferences, such as fashion, facial expression, and makeup. These preferences depend on many factors including a person\u27s sense of style, what they are doing, and the weather. These factors, in turn, are dependent upon geographic location and time. In our work, we build computational models to learn the relationship between human appearance, geographic location, and time. The primary contributions are a framework for collecting and processing geotagged imagery of people, a large dataset collected by our framework, and several generative and discriminative models that use our dataset to learn the relationship between human appearance, location, and time. Additionally, we build interactive maps that allow for inspection and demonstration of what our models have learned