Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax order to improve their display. Uncheck the box turn off. This feature requires Javascript. Click on a formula zoom.

In this letter, the recently developed extreme gradient boosting (Xgboost) classifier is implemented in a very high resolution (VHR) object-based urban land use-land cover application. detail, we investigated sensitivity of Xgboost to various sample sizes, as well feature selection (FS) by applying standard technique, correlation-based FS. We compared with benchmark classifiers such random forest (RF) and support vector machines (SVMs). The methods are applied VHR imagery two sub-Saharan...

This study evaluates the impact of four feature selection (FS) algorithms in an object-based image analysis framework for very-high-resolution land use-land cover classification. The selected FS algorithms, correlation-based selection, mean decrease accuracy, random forest (RF) based recursive elimination, and variable using forest, were tested on extreme gradient boosting, support vector machine, K-nearest neighbor, RF, partitioningclassifiers, respectively. results demonstrate that...

Since 1999, very high spatial resolution satellite data represent the surface of Earth with more detail. However, information extraction by per pixel multispectral classification techniques proves to be complex owing internal variability increase in land-cover units and weakness spectral resolution. Image segmentation before was proposed as an alternative approach, but a large variety algorithms were developed during last 20 years, comparison their implementation on images is necessary. In...

Up-to-date and reliable land-use information is essential for a variety of applications such as planning or monitoring the urban environment. This research presents workflow mapping land use at street block level, with focus on residential use, using very-high resolution satellite imagery derived land-cover maps input. We develop processing chain automated creation polygons from OpenStreetMap ancillary data. Spatial metrics other features are computed, followed by feature selection that...

With the emergence of very high spatial resolution satellite images, gap which existed between images and aerial photographs has decreased. A study potential these for tree species in “monoculture stands” identification was conducted. Two Ikonos were acquired, one June 2000 other October 2000, an 11- by 11-km area covering Sonian Forest southeastern part Brussels-Capital region (Belgium). The two orthorectified using a digital elevation model 1256 geodetic control points. carried out...

The limited spatial resolution of satellite images used to be a problem for the adequate definition urban environment. This was expected solved with availability very high (IKONOS, QuickBird, OrbView‐3). However, these space‐borne sensors are four multi‐spectral bands and may have specific limitations as far detailed area mapping is concerned. It therefore essential combine spectral information other information, such features in visual interpretation (e.g. degree kind texture shape)...

This study presents the development of a semi-automated processing chain for urban object-based land-cover and land-use classification. The is implemented in Python relies on existing open-source software GRASS GIS R. complete tool available open access adaptable to specific user needs. For automation purposes, we developed two add-ons enabling users (1) optimize segmentation parameters an unsupervised manner (2) classify remote sensing data using several individual machine learning...

Land cover Classified maps obtained from deep learning methods such as Convolutional neural networks (CNNs) and fully convolutional (FCNs) usually have high classification accuracy but with the detailed structures of objects lost or smoothed. In this work, we develop a methodology based on (FCN) that is trained in an end-to-end fashion using aerial RGB images only input. Skip connections are introduced into FCN architecture to recover spatial details lower layers. The experiments conducted...

Many cities in low- and medium-income countries (LMICs) are facing rapid unplanned growth of built-up areas, while detailed information on these deprived urban areas (DUAs) is lacking. There exist visible differences housing conditions spaces, linked to deprivation. However, the appropriate geospatial for unravelling deprivation typically not available DUAs LMICs, constituting an urgent knowledge gap. The objective this study apply deep learning techniques morphological analysis identify...

Separation of savanna land cover components is challenging due to the high heterogeneity this landscape and spectral similarity compositionally different vegetation types. In study, we tested usability very spatial resolution WorldView-2 (WV-2) imagery classify African in wet dry season. We compared performance Object-Based Image Analysis (OBIA) pixel-based approach with several algorithms: k-nearest neighbor (k-NN), maximum likelihood (ML), random forests (RF), classification regression...

To classify Very-High-Resolution (VHR) imagery, Geographic Object Based Image Analysis (GEOBIA) is the most popular method used to produce high quality Land-Use/Land-Cover maps. A crucial step in GEOBIA appropriate parametrization of segmentation algorithm prior classification. However, little effort has been made automatically optimize algorithms an unsupervised and spatially meaningful manner. So far, Unsupervised Segmentation Parameter Optimization (USPO) techniques, assume spatial...

Goma city, at the eastern border of DRCongo, is highly exposed to natural hazards, especially from Nyiragongo volcano, located directly North it. In January 2002, city centre was devastated by lava flows and several thousands people were temporarily displaced. Defining quantifying population vulnerability flow hazards in particular, a crucial element evaluate manage risk. This paper aims assessing facing volcanic Goma, its spatial variation across order support risk prevention management...

Built-up layers derived from medium resolution (MR) satellite information have proven their contribution to dasymetric mapping, but suffer important limitations when working at the intra-urban level, mainly due difficulty in capturing whole range of variation terms built-up densities. In this regard, very-high (VHR) remote sensing is known for its ability better capture small variations densities and derive detailed urban land use, which plead favor use mapping populations. paper, we compare...

Abstract Deprived urban areas, commonly referred to as ‘slums,’ are the consequence of unprecedented urbanisation. Previous studies have highlighted potential Artificial Intelligence (AI) and Earth Observation (EO) in capturing physical aspects deprivation. However, little research has explored AI’s ability predict how locals perceive This aims develop a method citizens’ perception deprivation using satellite imagery, citizen science, AI . A score was computed from slum-citizens’ votes....

Abstract The authors examine the local impact of change in impervious surfaces Brussels capital region (BCR), Belgium, on trends maximum, minimum, and mean temperatures between 1960 1999. Specifically, data are combined from remote sensing imagery a land surface model including state-of-the-art urban parameterization—the Town Energy Balance scheme. To (i) isolate effects growth near-surface temperature independent atmospheric circulations (ii) be able to run over very long period without any...