DCP-Net: A Distributed Collaborative Perception Network for Remote Sensing Semantic Segmentation

Onboard intelligent processing is widely applied in emergency tasks in the field of remote sensing. However, it is predominantly confined to an individual platform with a limited observation range as well as susceptibility to interference, resulting in limited accuracy. Considering the current state of multi-platform collaborative observation, this article innovatively presents a distributed collaborative perception network called DCP-Net. Firstly, the proposed DCP-Net helps members to enhance perception performance by integrating features from other platforms. Secondly, a self-mutual information match module is proposed to identify collaboration opportunities and select suitable partners, prioritizing critical collaborative features and reducing redundant transmission cost. Thirdly, a related feature fusion module is designed to address the misalignment between local and collaborative features, improving the quality of fused features for the downstream task. We conduct extensive experiments and visualization analyses using three semantic segmentation datasets, including Potsdam, iSAID and DFC23. The results demonstrate that DCP-Net outperforms the existing methods comprehensively, improving mIoU by 2.61%~16.89% at the highest collaboration efficiency, which promotes the performance to a state-of-the-art level

Advanced visual slam and image segmentation techniques for augmented reality

Augmented reality can enhance human perception to experience a virtual-reality intertwined world by computer vision techniques. However, the basic techniques cannot handle complex large-scale scenes, tackle real-time occlusion, and render virtual objects in augmented reality. Therefore, this paper studies potential solutions, such as visual SLAM and image segmentation, that can address these challenges in the augmented reality visualizations. This paper provides a review of advanced visual SLAM and image segmentation techniques for augmented reality. In addition, applications of machine learning techniques for improving augmented reality are presented

Understanding cities with machine eyes: A review of deep computer vision in urban analytics

Modelling urban systems has interested planners and modellers for decades. Different models have been achieved relying on mathematics, cellular automation, complexity, and scaling. While most of these models tend to be a simplification of reality, today within the paradigm shifts of artificial intelligence across the different fields of science, the applications of computer vision show promising potential in understanding the realistic dynamics of cities. While cities are complex by nature, computer vision shows progress in tackling a variety of complex physical and non-physical visual tasks. In this article, we review the tasks and algorithms of computer vision and their applications in understanding cities. We attempt to subdivide computer vision algorithms into tasks, and cities into layers to show evidence of where computer vision is intensively applied and where further research is needed. We focus on highlighting the potential role of computer vision in understanding urban systems related to the built environment, natural environment, human interaction, transportation, and infrastructure. After showing the diversity of computer vision algorithms and applications, the challenges that remain in understanding the integration between these different layers of cities and their interactions with one another relying on deep learning and computer vision. We also show recommendations for practice and policy-making towards reaching AI-generated urban policies

Risk assessment of foot and mouth disease in the border between Brazil and Paraguay : a geographical approach

La fièvre aphteuse (FA) est l'une des maladies infectieuses les plus importantes qui affectent les animaux biongulés. Le Brésil est libre avec vaccination depuis 2001, mais en 2005, une épidémie est survenue à la frontière entre le Brésil et le Paraguay. Identifier les exploitations agricoles ou des espaces géographiques qui sont le plus à risque de fièvre aphteuse en particulier dans les régions frontalières est l'un des principaux objectifs du service vétérinaire officiel du Brésil et aussi d'autres pays d'Amérique du Sud. Les indicateurs utilisés par le gouvernement brésilien pour indentifier les zones à risque de fièvre aphteuse prennent en considération essentiellement des informations au niveau du troupeau (structure du troupeau, la présence de jeunes animaux, rapport vache / veau, etc.). Dans ce contexte, l'objectif principal de notre recherche est d'élaborer un cadre pour l'évaluation des risques de fièvre aphteuse à la frontière entre le Brésil et le Paraguay prenant en compte les aspects géographiques liés aux systèmes de production. Afin d'atteindre cet objectif, l'étude a été divisée en trois articles. Le premier article dresse un aperçu concernant les pratiques d'hygiène et de contrôle de la FA dans cette zone particulière. Quatre-vingt-sept agriculteurs ont été interrogés sur cinq thèmes principaux: la caractérisation des agriculteurs, les indicateurs sanitaires, la vaccination de la fièvre aphteuse, la circulation des personnes et des animaux ainsi que l'opinion des agriculteurs sur les risques d'introduction de la FA. Les résultats montrent que les agriculteurs sont conscients de leur rôle dans le combat contre la fièvre aphteuse. Il montre également que les agriculteurs, surtout les petits, ont besoin d'être mieux soutenus. Ils n'ont toujours pas de contrôle sanitaire formel. Ils ont besoin de formation et d’un constant soutien. Même si cette région a le même statut sanitaire que le reste du Mato Grosso do Sul, qui est libre de fièvre aphteuse avec vaccination, le contrôle et les différentes mesures sanitaires doivent se poursuivre. Le deuxième article explore la possibilité d'utiliser la télédétection pour cartographier et pour surveiller les zones de pâturage afin d’établir des modèles localisés de prédiction de densité de bétail. Un modèle statistique afin de prédire le nombre de bovins en fonction de la superficie de pâturage déclarée par les agriculteurs, a été réalisé sur la base de données officielle concernant l'élevage de la zone d‘étude. Finalement, ce modèle a été appliqué aux zones de pâturage détectées par classification orientée objet pour prédire la densité bovine. Les résultats indiquent que la méthodologie utilisée pour estimer la densité du bétail peut être utilisée dans des régions où l'information sur l'emplacement et la densité de ferme d'élevage est inexistante. Dans le troisième article, nous avons testé l'approche à majorité floue d’analyse multicritère de décision basée sur un système d’information geographique (SIG - AMC) afin de déterminer les zones à risque d'introduction de la FA. Deux scénarios ont été comparés, le premier basé sur la ferme (où l'information officielle est disponible) et le second basé sur la télédétection (où seulement l'information géographique est disponible). Les cartes obtenues mettent en évidence la forte hétérogénéité spatiale du risque d'introduction de la FA. Une corrélation positive a été observée entre les scénarios basés sur la ferme et les scénarios basés sur la télédétection. Cette étude fournit un cadre alternatif pour détecter les zones à risque de FA et de cette manière pour renforcer les mesures sanitaires brésiliennes. Il a également un grand potentiel pour être extrapolé à d'autres régions ayant des caractéristiques similaires mais où des informations au niveau du troupeau sont rares, ou inexistantes, comme d'autres régions reculées du Brésil ou d'autres pays d'Amérique du Sud Mots-clés : régions frontalières, analyse multicritère à la décision, télédétection, analyse de risques de fièvre aphteuseFoot and mouth disease (FMD) is one of the most important infectious diseases that can affect cloven hoofed animals. Brazil is free with vaccination since 2001, but in 2005 an outbreak occurred in the border between Brazil and Paraguay. Identifying farms or geographic spaces that are more at risk of FMD, especially in border regions, is one of the main goals of official veterinary service from Brazil and other South American countries. Indicators used by the Brazilian government to indentify FMD risk areas takes into consideration basic information at herd level. For these reasons, the principal objective of this research was to elaborate a framework for FMD risk assessment in the frontier between Brazil and Paraguay that takes in account geographic aspects associated with production systems information. In order to accomplish this objective, the study was divided in three articles. The first article draws an overview regarding sanitary practices and FMD control in this particular zone. Eighty seven farmers were interviewed regarding five main subjects: farmers’ characterization, sanitary indicators, FMD disease vaccination, people and animal movements and farmer’s opinions about FMD risks of introduction. The results show that farmers are conscious of their roles in FMD control. It also shows that among small farmers there is a need to be better assisted. Such farmers lack formal sanitary controls and they need constant training and support. Even if this region has the same sanitary status as the rest of Mato Grosso do Sul State (which is FMD free with vaccination), differentiated sanitary measures and control should continue. The second article explores the potential use of remote sensing to map and monitor pasture areas and to establish models for predicting cattle density and location. A statistical model to predict numbers of cattle in function of declared pasture area by the farmers was produced on the basis of Brazilian official livestock databases for the studied area. Finally, this model was applied to the pasture areas detected by oriented based classification to predict cattle density. The results indicate that the methodology used for estimating cattle density has the potential to be applied in regions where no information about farm location and cattle density exists. In the third article the fuzzy majority approach for GIS based multicriteria decision analysis (GIS – MCDA) was tested to determine risk areas of FMD introduction. Two main scenarios were compared: a farm-based one (where official information is available) and a remote sensing-based one (where only geographic information is available). Resulting maps highlighted a strong spatial heterogeneity in the risk of FMD introduction. A positive correlation was observed between farm-based scenarios and remote sensing-based scenarios. This study provides an alternative framework to detect areas of higher risk of FMD and by this way reinforce Brazilian sanitary measures. It also has great potential to be extrapolated for other regions with similar characteristics but where information at herd level are sparse or inexistent such as remote regions of Brazil and other South American countries. Key-words: border regions, multicriteria decision analysis, remote sensing, FMD risk assessment

Monitoring Global Forest Land-Use and Change

Earth’s forests contain nearly three-fourths of the World’s floral and faunal diversity, function as a large carbon sink capable of mitigating the effects of global climate change, affect local and regional physical and chemical cycles and provide wood and non-wood products. However, humans are now capable of modifying their environment in ways more impactful and at rates faster than at any other time in history. Consistent and comparable estimates of global forest land-use and change are critical for monitoring human impacts on the Earth system. International treaties and reporting requirements aimed at safeguarding the delivery of forest-related ecosystem services depend on such estimates for measuring progress against their stated goals. Many existing studies have estimated tree cover and change at a variety of spatial scales from local to global. However, this existing research focuses largely on land cover classification, but generally lacks ecological context for estimating true human land use. The objective of this dissertation is to address this gap by exploring how forest land use can be mapped and monitored using medium spatial resolution optical satellite imagery in order to estimate forest land use change over time for large geographic areas. First, the effects of clouds, cloud shadows and missing data were analyzed to determine the amount of moderate spatial resolution, optical satellite data needed to detect and map land cover changes over large, spatially continuous areas on frequent time intervals. Second, an alternative method to spatially exhaustive mapping was developed and tested for estimating land cover and land use change globally employing object-based image analysis and a sample-based estimation approach. The method facilitated expert human intervention to identify true land use change in an operational way. Finally, these methods were applied to a globally distributed sample of remotely sensed data for the time periods 1990, 2000 and 2005. The results of this research produced the first consistent and comparable global time-series dataset of forest land-use estimates

Discovering similarities in Landsat satellite images using the Kmeans method

This article different ways for the treatment and identification of similarities in satellite images. By means of the systematic review of the literature it is possible to know the different existing forms for the treatment of this type of objects and by means of the implementation that is described, the operation of the K-means algorithm is shown to help the segmentation and analysis of characteristics associated to the color. In this type of objects, a descriptive analysis of the results thrown by the method is finally carried out