Publication Download BibTex Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data. However, they ...a new framework that incorporates spatial context and correlation explicitly into the models. Building on recent advances in geospatial auxiliary t...
Geospatial mappingThis paper presents a fast approximation method for Multidimensional Scaling (MDS)-based dimensionality reduction on large cartography datasets. Since MDS preserves data point distances, it is useful in application domains where geolocation data are critical. Typical relevant tasks include ...
geospatialneural networksremote sensingspace–timespatial analysisspatial databasespatial modelingspatiotemporal datasupervised learningtechnologyArtificial neural networks are computational models widely used in geospatial analysis for data classification, change detection, clustering, function approximation, and ...
To develop and validate the predictive models, a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed. The step-wise weight assessment ratio analysis (SWARA) was employed to investigate ...
The result shows the ability of the toolbox to produce suitability maps for landfill sites. 展开 关键词: GEOSPATIAL data DATA mining ARTIFICIAL neural networks DOI: 10.5194/isprs-archives-XLII-4-W1-199-2016 年份: 2016 收藏 引用 批量引用 报错 分享 ...
The present study uses a deep learning convolutional neural networks (CNN) algorithm, which is among the newer and most powerful algorithms in big data sets, to develop a flood susceptibility map for Iran. A total of 2769 records were collected from flood locations across the entire country; ...
Minetto R, Segundo MP, Sarkar S (2019) Hydra: An ensemble of convolu- tional neural networks for geospatial land classification. IEEE Trans Geosci Remote Sens 57(9):6530–6541 Article Chen Y, Wang Y, Gu Y, He X, Ghamisi P, Jia X (2019) Deep learning ensem- ble for hyperspectral ...
To develop and validate the predictive models, a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed. The step-wise weight assessment ratio analysis (SWARA) was employed to investigate ...
More specifically, these methods enable the usage of neural networks for the discovery of physically meaningful relationships within geoscientific data. Neural networks, also occasionally dubbed “deep learning” (LeCun et al., 2015), are one of the most versatile types of machine learning methods ...
We begin by proposing a classical Recurrent Neural Networks (RNNs) architecture for soil profiles prediction, followed by the design of its quantum counterpart with QRNNs. Focusing on the application of our model in Türkiye, we leverage geospatial data from diverse sources, including climate, ...