Georeferencing for Planning Underground Networks

Опубликована: Янв. 27, 2024

Often the problems related to planning of electrical networks, drinking water (sewage), data, and transportation, use ideal models that are not geo-referenced.However, present work exposes a suitable innovative methodology achieve data analytics from OSM files.This geographic information file can be freely downloaded https : //www.openstreetmap.org/[1].Then, applying geo-referenced model network provides characteristics environment in x y coordinates.In this sense, using longitude latitude allows location with less error for distance calculations used optimal wireless sensors (smart meters), transformers, determination route burying power lines, as well determine population growth specific area, evaluation rescue zone or geographical areas vaccination campaigns, among other options, [2]- [4].Over time, scientific publications seek innovate concerning previous by community.Thus, applications distribution networks based on perform modeling deployment underground networks.Simulation tools such Cymdist [5]- [7] generally evaluate deployed model.In way, aim is serve minimum cost electric cable considering variables voltage drops [8], [9].

Язык: Английский

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Georeferencing for Planning Underground Networks

Опубликована: Фев. 8, 2024

Язык: Английский

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Towards a Deep Automatic Generation of Figure-ground Maps

ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, Год журнала: 2024, Номер X-4/W5-2024, С. 33 - 39

Опубликована: Июнь 27, 2024

Abstract. Figure-ground maps play a key role in many disciplines where urban planning or analysis is involved. In this context, the automatic generation of such with respect to certain requirements and constraints an important task. This paper presents first step towards deep figure-ground built density generated scenes controlled taken into account. preformed building upon Geographic Data Translation model which has been applied generate less available geospatial features, e.g. footprints, from more widely data, street network using conditional Generative Adversarial Networks. A novel processing approach introduced incorporate population accordingly. Furthermore, impact both level detail network, i.e. its sparsity density, spatial resolution training data on investigated. The qualitative results reveal obvious these parameters layout unbuilt areas. Our paves way for expansion existing districts by future neighbourhoods considering factors as further will be subject work.

Язык: Английский

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Satellite image manipulation detection in generative AI era

Опубликована: Ноя. 13, 2024

Generative Artificial Intelligence (AI) is becoming increasingly prevalent due to the availability of machine learning models, such as stable diffusion, and greater computational powers. While this has many advantages, it led maliciously generated images being created, AI-generated satellite imagery now an emerging threat. The National Geospatial-Intelligence Agency acknowledged that AI been utilised manipulate for malicious purposes not yet widespread. However, there a high likelihood will be, ever-increasing prevalence social media. This paper proposes development new dataset containing have synthetically manipulated using generative models since are currently none publicly available. We also propose deep-learning-based detection algorithm manipulation. research supports fight against misinformation help ensure remain objective source truth. work aims create benchmark detecting images.

Язык: Английский

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Fréchet distance in spatial data quality

Applied Geomatics, Год журнала: 2024, Номер 17(1), С. 17 - 34

Опубликована: Дек. 12, 2024

Язык: Английский

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