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Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data 总被引：1，自引：0，他引：1
Shivangi Srivastava John E. Vargas Muñoz Sylvain Lobry Devis Tuia 《International journal of geographical information science》2020,34(6):1117-1136
ABSTRACTWe study the problem of landuse characterization at the urban-object level using deep learning algorithms. Traditionally, this task is performed by surveys or manual photo interpretation, which are expensive and difficult to update regularly. We seek to characterize usages at the single object level and to differentiate classes such as educational institutes, hospitals and religious places by visual cues contained in side-view pictures from Google Street View (GSV). These pictures provide geo-referenced information not only about the material composition of the objects but also about their actual usage, which otherwise is difficult to capture using other classical sources of data such as aerial imagery. Since the GSV database is regularly updated, this allows to consequently update the landuse maps, at lower costs than those of authoritative surveys. Because every urban-object is imaged from a number of viewpoints with street-level pictures, we propose a deep-learning based architecture that accepts arbitrary number of GSV pictures to predict the fine-grained landuse classes at the object level. These classes are taken from OpenStreetMap. A quantitative evaluation of the area of Île-de-France, France shows that our model outperforms other deep learning-based methods, making it a suitable alternative to manual landuse characterization. 相似文献
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A semisupervised support vector machine is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictions. 相似文献
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First principles calculations have been used to investigate the condensation reactions of hydrated calcium bicarbonate monomers in a simulated aqueous environment. The reaction pathway for the calcium bicarbonate dimerization process has been computed at the density functional theory-PBE level with the COSMO dielectric continuum model to simulate the hydrated environment. The results indicate that calcium bicarbonate dimers form via an associative mechanism: the first step involves a sevenfold calcium bicarbonate intermediate followed by the loss of one water molecule from the first coordination shell of calcium. Both steps are characterised by a low energy barrier of approximately 2 kcal mol−1, suggesting that the dimerization process is not kinetically hindered in aqueous solution. However, the Gibbs free energies for the condensation reactions to form the calcium bicarbonate dimers and the species Ca(HCO3)2(H2O)4, Ca(HCO3)3(H2O)3− and Ca2(HCO3)(H2O)103+, computed using the PBE and mPW1B95 density functional theory levels for the gas-phase component and the UAHF-CPCM solvation model for the hydration contribution, are all positive, which indicates that the formation of these early calcium bicarbonate clusters is thermodynamically unfavourable in aqueous solutions. Our calculations therefore suggest that the oligomerization of calcium carbonate is not spontaneous in water, at the conditions considered in our simulations, i.e. T = 298 K and neutral pH, which indicates that the nucleation of calcium carbonate cannot occur through a homogeneous process when calcium-bicarbonate ion pairs are the major source of CaCO3 in the aqueous environment. 相似文献
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Loris Foresti Devis Tuia Mikhail Kanevski Alexei Pozdnoukhov 《Stochastic Environmental Research and Risk Assessment (SERRA)》2011,25(1):51-66
This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool.
The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a
given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear
robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL
as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains.
Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain
features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without
degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training
data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit
a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region. 相似文献
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