Search Results (39)

Although walking methodologies (WMs) and machine learning (ML) have been objects of interest for urban scholars, it is difficult to find research that integrates both. We propose a ‘cyborg walk’ method and apply it to studying litter in public spaces. Walking routes are created based on an unsupervised learning algorithm (k-means) to classify public spaces. Then, a deep learning model (YOLOv5) is used to collect data from geotagged photos taken by an automatic Insta360 X3 camera worn by human walkers. Results from image recognition have an accuracy between 83.7% and 95%, which is similar to what is validated by the literature. The data collected by the machine are automatically georeferenced thanks to the metadata generated by a GPS attached to the camera. WMs could benefit from the introduction of ML for informative route optimisation and georeferenced visual data quantification. The links between these findings and the existing WM literature are discussed, reflecting on the parallels between this ‘cyborg walk’ experiment and the seminal cyborg metaphor proposed by Donna Haraway. Full article

User-generated geo-tagged photos (UGPs) have emerged as a valuable tool for analyzing large-scale tourist place emotions with unprecedented detail. This process involves extracting and analyzing human emotions associated with specific locations. However, previous studies have been limited to analyzing individual faces in the UGPs. This approach falls short of representing the contextual scene characteristics, such as environmental elements and overall scene context, which may contain implicit emotional knowledge. To address this issue, we propose an innovative computational framework for global tourist place emotion analysis leveraging UGPs. Specifically, we first introduce a Multi-view Graph Fusion Network (M-GFN) to effectively recognize multi-view emotions from UGPs, considering crowd emotions and scene implicit sentiment. After that, we designed an attraction-specific emotion index (AEI) to quantitatively measure place emotions based on the identified multi-view emotions at various tourist attractions with place types. Complementing the AEI, we employ the emotion intensity index (EII) and Pearson correlation coefficient (PCC) to deepen the exploration of the association between attraction types and place emotions. The synergy of AEI, EII, and PCC allows comprehensive attraction-specific place emotion extraction, enhancing the overall quality of tourist place emotion analysis. Extensive experiments demonstrate that our framework enhances existing place emotion analysis methods, and the M-GFN outperforms state-of-the-art emotion recognition methods. Our framework can be adapted for various geo-emotion analysis tasks, like recognizing and regulating workplace emotions, underscoring the intrinsic link between emotions and geographic contexts. Full article

In the pursuit of sustainable national park management, park managers need to understand the interests and activities of their diverse visitors in order to conserve the natural environment and offer a better visitor experience. This study aimed to examine the effectiveness of using non-geotagged social media data from posts by park visitors for park management in comparison with geotagged data, which has been studied more extensively. We compared (1) visitors’ sociodemographic characteristics between geotagged and non-geotagged social media users through an onsite survey in Nikko National Park, Japan, and (2) the content of geotagged and non-geotagged photos shared within the study area on X (formerly Twitter). Our results showed that visitors in their 30s and 40s and foreign visitors had a greater tendency to use geotags. Non-geotagged photos more frequently and deeply capture nature-based activities and interests, including activities on trails, such as mountain climbing and hiking, and an interest in diverse animals and plants and landscapes that are less accessible. These findings indicate that non-geotagged social media data may have less age and nationality bias and advantages over the more widely-used geotagged data in capturing various nature-based experiences offered by national parks. Leveraging both geotagged and non-geotagged data can enable park managers to implement sustainable practices catering to a broader range of visitor interests and activities, contributing to the overarching goal of sustaining the natural environment while also enriching the visitor experience within national parks. Full article

Social media data, such as photos and status posts, can be tagged with location information (geotagging). This geotagged information can be used for urban spatial analysis to explore neighborhood characteristics or mobility patterns. With increasing rural-to-urban migration, there is a need for comprehensive data capturing the complexity of urban settings and their influence on human experiences. Here, we share an urban image stimulus set from the city of Lisbon that researchers can use in their experiments. The stimulus set consists of 160 geotagged urban space photographs extracted from the Flickr social media platform. We divided the city into 100 × 100 m cells to calculate the cell image density (number of images in each cell) and the cell green index (Normalized Difference Vegetation Index of each cell) and assigned these values to each geotagged image. In addition, we also computed the popularity of each image (normalized views on the social network). We also categorized these images into two putative groups by photographer status (residents and tourists), with 80 images belonging to each group. With the rise in data-driven decisions in urban planning, this stimulus set helps explore human–urban environment interaction patterns, especially if complemented with survey/neuroimaging measures or machine-learning analyses. Full article

An aerial survey method called the Landscape Assessment System (LAS) was used to assess mountain pine beetle (Dendroctonus ponderosae)-caused mortality of whitebark pine (Pinus albicaulis) across the Greater Yellowstone Ecosystem (59,000 km²; GYE). This consisted of 11,942 km of flightlines, along which 4434 geo-tagged, oblique aerial photos were captured and processed. A mortality rating of none to severe (0–4.0 recent attack or 5.0–5.4 old attack) was assigned to each photo based on the amount of red (recent attack) and gray (old attack) trees visible. The method produced a photo inventory of 74 percent of the GYE whitebark pine distribution. For the remaining 26 percent of the distribution, mortality levels were estimated based on an interpolated mortality surface. Catchment-level results combining the photo-inventoried and interpolated mortality indicated that 44 percent of the GYE whitebark pine distribution showed severe old attack mortality (5.3–5.4 rating), 37 percent showed moderate old attack mortality (5.2–5.29 rating), 19 percent showed low old attack mortality (5.1–5.19 rating) and less than 1 percent showed trace levels of old attack mortality (5.0–5.09). No catchments were classified as recent attacks indicating that the outbreak of the early 2000’s has ended. However, mortality continues to occur as chronic sub-outbreak-level mortality. Ground verification using field plots indicates that higher LAS mortality values are moderately correlated with a higher percentage of mortality on the ground. Full article

Massive and high-quality in situ data are essential for Earth-observation-based agricultural monitoring. However, field surveying requires considerable organizational effort and money. Using computer vision to recognize crop types on geo-tagged photos could be a game changer allowing for the provision of timely and accurate crop-specific information. This study presents the first use of the largest multi-year set of labelled close-up in situ photos systematically collected across the European Union from the Land Use Cover Area frame Survey (LUCAS). Benefiting from this unique in situ dataset, this study aims to benchmark and test computer vision models to recognize major crops on close-up photos statistically distributed spatially and through time between 2006 and 2018 in a practical agricultural policy relevant context. The methodology makes use of crop calendars from various sources to ascertain the mature stage of the crop, of an extensive paradigm for the hyper-parameterization of MobileNet from random parameter initialization, and of various techniques from information theory in order to carry out more accurate post-processing filtering on results. The work has produced a dataset of 169,460 images of mature crops for the 12 classes, out of which 15,876 were manually selected as representing a clean sample without any foreign objects or unfavorable conditions. The best-performing model achieved a macro F1 (M-F1) of 0.75 on an imbalanced test dataset of 8642 photos. Using metrics from information theory, namely the equivalence reference probability, resulted in an increase of 6%. The most unfavorable conditions for taking such images, across all crop classes, were found to be too early or late in the season. The proposed methodology shows the possibility of using minimal auxiliary data outside the images themselves in order to achieve an M-F1 of 0.82 for labelling between 12 major European crops. Full article

Large-scale, high-input, and intensified agriculture poses threats to sustainable agroecosystems and their inherent biodiversity. The EU Common Agricultural Policy (CAP) covers a great number of nature conservation programs (Agri-Environment and Climate Measures, AECM) aiming to encourage sustainable agriculture. Currently, farmers are not encouraged to broadly implement these measures due to the lack of structured information, overly complicated and unclear application procedures, and a high risk of sanctions. In addition, the current structures are associated with time-consuming monitoring and control procedures for the paying agencies. Digital technologies can offer valuable assistance to circumvent relevant barriers and limitations and support a broader uptake of AECM. NatApp is a digital tool that supports and guides farmers through the complete process of choosing, applying, implementing, and documenting AECM on their fields in accordance with legal requirements in Germany. We introduce the concept of NatApp and analyze how it can simplify and encourage the uptake and implementation of AECM. This study identifies its unique features for the provision of information and documentation opportunities compared with other digital farming tools focused on sustainable agriculture and outline how it can support farmers to actively contribute to more sustainable agriculture. Full article

This paper maps the cultural ecosystem services (CES) of a well-known giant-wave hotspot located in Nazaré, Portugal. The paper adopts a qualitative approach combining an auto-ethnographic direct observation of a journey and the content analysis of photos and videos posted on the YouTube and Facebook pages of tourists and operators. A total of 44 geotagged photos from a sample of 6914 photos retrieved from Flickr allowed the classification and spatial distribution of several CES: (1) recreational—surf activities; (2) aesthetic—photography; (3) spiritual—dark tourism and risk recreation; (4) intangible heritage—maritime knowledge; (5) scientific—wave height forecast; (6) sense of place; and (7) social relations. The paper also proposes a theoretical framework that highlights the pull drivers (risk recreation, storm chasing, or spectacular death voyeurism) and the push drivers (e.g., marketing campaigns and wave forecasts alerts) that explain the behaviors of the big-wave spectators/chasers during the experience journey. Public decision-makers, destination marketing organizations, tourism operators, and business entrepreneurs must acknowledge the relevance of journey mapping in order to identify the moments of stress and the touchpoints associated with peak/positive experiences generated by these CES. This study confirms some push and pull factors assessed by previous studies. Full article

Unmanned aerial vehicles (UAVs), also known as drones, are important for several application domains, such as the military, agriculture, cultural heritage documentation, surveillance, and the delivery of goods/products/services. A drone’s trajectory can be enriched with external and heterogeneous data beyond latitude, longitude, and timestamp to create its semantic trajectory, providing meaningful and contextual information on its movement data, enabling decision makers to acquire meaningful and enriched contextual information about the current situation in the field of its operation and eventually supporting simulations and predictions of high-level critical events. In this paper, we present an ontology-based, tool-supported framework for the reconstruction, modeling, and enrichment of drones’ semantic trajectories. This framework extends MovingPandas, a widely used and open-source trajectory analytics and visualization tool. The presented research extends our preliminary work on drones’ semantic trajectories by contributing (a) an updated methodology for the reconstruction of drones’ trajectories from geo-tagged photos taken by drones during their flights in cases in which flight plans and/or real-time movement data have been lost or corrupted; (b) an enrichment of the reconstructed trajectories with external data; (c) the semantic annotation of the enriched trajectories based on a related ontology; and (d) the use of SPARQL queries to analyze and retrieve knowledge related to the flight of a drone and the field of operations (context). An evaluation of the presented framework, namely, ReconTraj4Drones, was conducted against several criteria, using real and open datasets. Full article

The stay areas in walking tours are the service and management unit of recreational walking in metropolitan areas. The rational characterization of stay areas in walking tours is of great significance for developing local tourism, constructing appropriate public facilities, optimizing the configuration of tourist elements, and improving facility efficiency. The existing research focuses mainly on functional, top-down classifications of tourism, tourist behavior patterns, and route designs, but it has left tourists’ stay areas largely unaddressed. To fill this gap, we propose a new framework for the interpretation of stay areas in walking tours based on GPS trajectory data and accompanying photos uploaded by users. Taking the Zhuhai–Macao metropolitan area as an example, we first captured the stay points and clustered them to the walking tour stay areas using DBSCAN. The characteristics of the stay areas were then collected, and a hierarchical analysis was conducted in terms of spatial features and geotagged photos. The results show that the stay areas can be grouped into six categories displaying obvious differences in spatial distribution, landscape features, and tourist activities. We also found the connections between Zhuhai City and the Macao Special Administrative Region (SAR) to be relatively weak. In conclusion, our results can contribute to tourism planning as well as the further management and allocation of recreational service facilities in the area researched. Full article

This study aims to explore tourism changes in coastal tourism destinations before and during the COVID-19 pandemic from the perspective of regional resilience. A mixed method of a social network and spatial analysis was used to evaluate inbound tourists’ geotagged photos of Indonesia on Flickr from 2018–2022 as metadata. The DBSCAN algorithm and Markov chains were used to comprehensively analyze the hotspot areas and the patterns of tourism movement trajectories amid a complicated recovery. The results demonstrate that: (1) The distribution of geotagged photos before and during the pandemic generally exhibited stage and regional unevenness. The main clusters were Java and the Nusa Tenggara Islands, with the rest displaying a scattered distribution. (2) The tourism flow network was unevenly distributed, and the nodes had obvious core and edge areas. Owing to the crisis, the tourism flow network realized a change in form from network to line and point. (3) Its impact on Indonesian inbound tourism may persist in the short term, and the volatility of national anti-pandemic policies influences the resilience of tourism flow during COVID-19. The dominance of the core nodes highlights the network’s resistance to disruptions due to the prominence of the location of network connections during the pandemic, and marginal nodes reflect the vulnerability to pandemic shocks owing to the hypocentricity of the nodes and the thinness of the connections within and outside the islands. These results provide marketing and promotion policies for the sustainable development of coastal areas. Full article

Forest landscape preference studies have an important role and significance for forest landscape conservation, quality improvement and utilization. However, there are few studies on objective forest landscape preferences from the perspective of plants and using photos. This study relies on Deep Learning technology to select six case sites in China and uses geotagged photos of forest landscapes posted by the forest recreationists on the “2BULU” app as research objects. The preferences of eight forest landscape scenes, including look down landscape, look forward landscape, look up landscape, single-tree-composed landscape, detailed landscape, overall landscape, forest trail landscape and intra-forest landscape, were explored. It also uses Deepsentibank to perform sentiment analysis on forest landscape photos to better understand Chinese forest recreationists’ forest landscape preferences. The research results show that: (1) From the aesthetic spatial angle, people prefer the flat view, while the attention of the elevated view is relatively low. (2) From the perspective of forest scale and level, forest trail landscape has a high preference, implying that trail landscape plays an important role in forest landscape recreation. The landscape within the forest has a certain preference, while the preference of individual, detailed and overall landscape is low. (3) Although forest landscape photographs are extremely high in positive emotions and emotional states, there are also negative emotions, thus, illustrating that people’s preferences can be both positive and negative. Full article

The built environment reshapes various scenes that can be perceived, experienced, and interpreted, which are known as city images. City images emerge as the complex composite of various imagery elements. Previous studies demonstrated the coincide between the city images produced by experts with prior knowledge and that are extracted from the high-frequency photo contents generated by citizens. The realistic city images hidden behind the volunteered geo-tagged photos, however, are more complex than assumed. The dominating elements are only one side of the city image; more importantly, the interactions between elements are also crucial for understanding how city images are structured in people’s minds. This paper focuses on the composition of city image–the various interactions between imagery elements and areas of a city. These interactions are identified as four aspects: co-presence, hierarchy, heterogeneity, and differentiation, which are quantified and visualized respectively as correlation network, dendrogram, spatial clusters, and scattergrams in a framework using scene recognition with volunteered and georeferenced photos. The outputs are interdependent elements, typologies of elements, imagery areas, and preferences for groups, which are essential for urban design processes. In the application in Central Beijing, the significant interdependency between two elements is complex and is not necessarily an interaction between the elements with higher frequency only. The main typologies and the principal imagery elements are different from what were prefixed in the image recognition model. The detected imagery areas with adaptive thresholds suggest the spatially varying spill over effects of named areas and their typologies can be well annotated by the detected principal imagery elements. The aggregation of the data from different social media platforms is proven as a necessity of calibrating the unbiased scope of the city image. Any specific data can hardly capture the whole sample. The differentiation across the local and non-local is found to be related to their preference and activity space. The results provide more comprehensive insights on the complex composition of city images and its effects on placemaking. Full article

Density-based clustering algorithms have been the most commonly used algorithms for discovering regions and points of interest in cities using global positioning system (GPS) information in geo-tagged photos. However, users sometimes find more specific areas of interest using real objects captured in pictures. Recent advances in deep learning technology make it possible to recognize these objects in photos. However, since deep learning detection is a very time-consuming task, simply combining deep learning detection with density-based clustering is very costly. In this paper, we propose a novel algorithm supporting deep content and density-based clustering, called deep density-based spatial clustering of applications with noise (DeepDBSCAN). DeepDBSCAN incorporates object detection by deep learning into the density clustering algorithm using the nearest neighbor graph technique. Additionally, this supports a graph-based reduction algorithm that reduces the number of deep detections. We performed experiments with pictures shared by users on Flickr and compared the performance of multiple algorithms to demonstrate the excellence of the proposed algorithm. Full article

According to various sources, Southern Morocco has stood out as an outstanding tourist destination in recent decades, with global appeal. Dakhla City, including Dakhla Bay, classified by the Convention on Wetlands in 2005 as a Wetland of International Importance, offers visitors various entertainment opportunities at many city sites. Therefore, human activity and social benefits should be considered in conjunction with the need to safeguard the ecosystems and maintain the Ecosystem Services (ES). This study aims to provide an overview of the tourism dynamics and hotspots related to cultural ecosystem services in Dakhla Bay. The landscape attributes are used along with an InVEST model to detect the distribution of preferences for the Cultural Ecosystem Services (CESs), map the hotspots, and identify the spatial correlations between features such as the landscape and visiting rate to understand which elements of nature attract people to the locations around the study area. Geotagged photos posted to the Flickr™ website between 2005 and 2017 were used to approximate the number of tourist visits. The results showed that tourism suffered several dips in 2005–2017 and that tourist visits are currently rising. Additionally, an estimated annual tourist visit rate shows that tourism in Dakhla Bay has been growing steadily by 2%. Full article