Digital elevation model correction in urban areas using extreme gradient boosting, land cover and terrain parameters

The accuracy of digital elevation models (DEMs) in urban areas is influenced by numerous factors including land cover and terrain irregularities. Moreover, building artifacts in global DEMs cause artificial blocking of surface flow pathways. This compromises their quality and adequacy for hydrological and environmental modelling in urban landscapes where precise and accurate terrain information is needed. In this study, the extreme gradient boosting (XGBoost) ensemble algorithm is adopted for enhancing the accuracy of two medium-resolution 30m DEMs over Cape Town, South Africa: Copernicus GLO-30 and ALOS World 3D (AW3D). XGBoost is a scalable, portable and versatile gradient boosting library that can solve many environmental modelling problems. The training datasets are comprised of eleven predictor variables including elevation, urban footprints, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover and bare ground cover. The target variable (elevation error) was calculated with respect to highly accurate airborne LiDAR. After training and testing, the model was applied for correcting the DEMs at two implementation sites. The correction achieved significant accuracy gains which are competitive with other proposed methods. The root mean square error (RMSE) of Copernicus DEM improved by 46 to 53% while the RMSE of AW3D DEM improved by 72 to 73%. These results showcase the potential of gradient boosted trees for enhancing the quality of DEMs, and for improved hydrological modelling in urban catchments.Comment: 8 page

Comparison of machine learning and statistical approaches for Digital Elevation Model (DEM) correction: interim results

The correction of digital elevation models (DEMs) can be achieved using a variety of techniques. Machine learning and statistical methods are broadly applicable to a variety of DEM correction case studies in different landscapes. However, a literature survey did not reveal any research that compared the effectiveness or performance of both methods. In this study, we comparatively evaluate three gradient boosted decision trees (XGBoost, LightGBM and CatBoost) and multiple linear regression for the correction of two publicly available global DEMs: Copernicus GLO-30 and ALOS World 3D (AW3D) in Cape Town, South Africa. The training datasets are comprised of eleven predictor variables including elevation, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector ruggedness measure, percentage bare ground, urban footprints and percentage forest cover as an indicator of the overland forest distribution. The target variable (elevation error) was derived with respect to highly accurate airborne LiDAR. The results presented in this study represent urban/industrial and grassland/shrubland/dense bush landscapes. Although the accuracy of the original DEMs had been degraded by several anomalies, the corrections improved the vertical accuracy across vast areas of the landscape. In the urban/industrial and grassland/shrubland landscapes, the reduction in the root mean square error (RMSE) of the original AW3D DEM was greater than 70%, after correction. The corrections improved the accuracy of Copernicus DEM, e.g., > 44% RMSE reduction in the urban area and >32% RMSE reduction in the grassland/shrubland landscape. Generally, the gradient boosted decision trees outperformed multiple linear regression in most of the tests

Analysing sub-standard areas using high resolution remote (VHR) sensing imagery

Urban planners and managers in developing countries often lack information on sub-standard areas. Base data mostly refer to relatively large and heterogeneous areas such as census or administrative wards, which are not necessarily a relevant geographical unit for representing and analysing deprivations. Moreover sub-standard areas are diverse, ranging from unrecognized slum areas (often in the proximity of hazardous areas) to regularized areas with poor basic services, and information on this diversity is difficult to capture. Sub-standard areas in Indian cities are typical examples of that diversity. In Mumbai, sub-standard areas range from unrecognized slum pockets to large regularized sub-standard areas. This paper explores the usage of the latest generation of very high (spatial and spectral) resolution satellite images using 8-Band images of WorldView-2 to analyse spatial characteristics of sub-standard areas. The research illustrates how VHR imagery helps in rapidly extracting spatial information on sub-standard areas as well as provides a better understanding of their morphological characteristics (e.g. built-up density, greenness and shape). For this study an East-West cross-section of Mumbai (India) was selected, which is strongly dominated by a variety of sub-standard areas. The research employed image segmentation to extract building footprints and used texture and spatial metrics to analyse physical characteristics of sub-standard areas, combined with purposely-collected ground-truth information. The results show the capacity of this methodology for characterizing the diversity of sub-standard areas in Mumbai, providing strategic information for urban management

Characterizing the Statistical Properties of SAR Clutter by Using an Empirical Distribution

The performances on the applications of synthetic aperture radar (SAR) data strongly depend on the statistical characteristics of the pixel amplitudes or intensities. In this paper, a new empirical model, called simply ℋo, has been proposed to characterize the statistical properties of SAR clutter data over the wide range of homogeneous, heterogeneous, and extremely heterogeneous returns of terrain classes. A particular case of the ℋodistribution is the well-known o distributions. We also derived analytically the estimators of the presented ℋo model by applying the “method of log cumulants” (MoLCs). The performance of the proposed model is verified by using some measured SAR images

The explainability of gradient-boosted decision trees for digital elevation model (dem) error prediction

Gradient boosted decision trees (GBDTs) have repeatedly outperformed several machine learning and deep learning algorithms in competitive data science. However, the explainability of GBDT predictions especially with earth observation data is still an open issue requiring more focus by researchers. In this study, we investigate the explainability of Bayesian-optimised GBDT algorithms for modelling and prediction of the vertical error in Copernicus GLO-30 digital elevation model (DEM). Three GBDT algorithms are investigated (extreme gradient boosting - XGBoost, light boosting machine – LightGBM, and categorical boosting – CatBoost), and SHapley Additive exPlanations (SHAP) are adopted for the explainability analysis. The assessment sites are selected from urban/industrial and mountainous landscapes in Cape Town, South Africa. Training datasets are comprised of eleven predictor variables which are known influencers of elevation error: elevation, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover, bare ground cover, and urban footprints. The target variable (elevation error) was calculated with respect to accurate airborne LiDAR. After model training and testing, the GBDTs were applied for predicting the elevation error at model implementation sites. The SHAP plots showed varying levels of emphasis on the parameters depending on the land cover and terrain. For example, in the urban area, the influence of vector ruggedness measure surpassed that of first-order derivatives such as slope and aspect. Thus, it is recommended that machine learning modelling procedures and workflows incorporate model explainability to ensure robust interpretation and understanding of model predictions by both technical and non-technical users.http://www.isprs.org/publications/archives.aspxGeography, Geoinformatics and MeteorologySDG-09: Industry, innovation and infrastructur

THE EXPLAINABILITY OF GRADIENT-BOOSTED DECISION TREES FOR DIGITAL ELEVATION MODEL (DEM) ERROR PREDICTION

Gradient boosted decision trees (GBDTs) have repeatedly outperformed several machine learning and deep learning algorithms in competitive data science. However, the explainability of GBDT predictions especially with earth observation data is still an open issue requiring more focus by researchers. In this study, we investigate the explainability of Bayesian-optimised GBDT algorithms for modelling and prediction of the vertical error in Copernicus GLO-30 digital elevation model (DEM). Three GBDT algorithms are investigated (extreme gradient boosting - XGBoost, light boosting machine – LightGBM, and categorical boosting – CatBoost), and SHapley Additive exPlanations (SHAP) are adopted for the explainability analysis. The assessment sites are selected from urban/industrial and mountainous landscapes in Cape Town, South Africa. Training datasets are comprised of eleven predictor variables which are known influencers of elevation error: elevation, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover, bare ground cover, and urban footprints. The target variable (elevation error) was calculated with respect to accurate airborne LiDAR. After model training and testing, the GBDTs were applied for predicting the elevation error at model implementation sites. The SHAP plots showed varying levels of emphasis on the parameters depending on the land cover and terrain. For example, in the urban area, the influence of vector ruggedness measure surpassed that of first-order derivatives such as slope and aspect. Thus, it is recommended that machine learning modelling procedures and workflows incorporate model explainability to ensure robust interpretation and understanding of model predictions by both technical and non-technical users

Digital elevation model correction in urban areas using extreme gradient boosting, land cover and terrain parameters

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLVIII-4/W9-2024 GeoAdvances 2024 – 8th International Conference on GeoInformation Advances, 11–12 January 2024, Istanbul, Türkiye.LIDAR data for the City of Cape Town was provided by the Information and Knowledge Management Department, City of Cape Town.The accuracy of digital elevation models (DEMs) in urban areas is influenced by numerous factors including land cover and terrain irregularities. Moreover, building artefacts in global DEMs cause artificial blocking of surface flow pathways. This compromises their quality and adequacy for hydrological and environmental modelling in urban landscapes where precise and accurate terrain information is needed. In this study, the extreme gradient boosting (XGBoost) ensemble algorithm is adopted for enhancing the accuracy of two medium-resolution 30-metre DEMs over Cape Town, South Africa: Copernicus GLO-30 and ALOS World 3D (AW3D). XGBoost is a scalable, portable and versatile gradient boosting library that can solve many environmental modelling problems. The training datasets are comprised of eleven predictor variables including elevation, urban footprints, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover and bare ground cover. The target variable (elevation error) was calculated with respect to highly accurate airborne LiDAR. After training and testing, the model was applied for correcting the DEMs at two implementation sites. The corrections achieved significant accuracy gains which are competitive with other proposed methods. There was a 46 – 53% reduction in the root mean square error (RMSE) of Copernicus DEM, and a 72 - 73% reduction in the RMSE of AW3D DEM. These results showcase the potential of gradient-boosted decision trees for enhancing the quality of global DEMs, especially in urban areas.The Commonwealth Scholarship Commission UK, and the University of Cape Town Postgraduate Funding Office.http://www.isprs.org/publications/archives.aspxhj2024Geography, Geoinformatics and MeteorologySDG-11:Sustainable cities and communitie

The Vulnerability of a City - Diagnosis from a Bird’s Eye View

When the tsunami in the Indian Ocean on 26 December 2004 hit the city of Banda Aceh on the island of Sumatra, Indonesia, neither the city administration nor its inhabitants, nor national or international organisations were prepared. Approximately 60.000 of the 260.000 inhabitants died, leaving other 30.000 homeless and causing an enormous impact on the local economy. In the aftermath of this event tsunami early warning system were developed and are operated today (e. g. the German Indonesian Tsunami Early Warning System – GITEWS (Lauterjung, 2005)). However, the problem of earthquake or tsunami prediction in a deterministic sense has not been solved yet (Zschau et al, 2002). Thus, an end-to-end tsunami early warning system includes not only the tsunami warning, but also the assessment of vulnerability, perception studies, evacuation modeling, eventually leading to technical requirements for monitoring stations and recommendations for adaptation and mitigation strategies (Taubenböck et al., 2009a). In this study we address several specific questions on the capabilities of one discipline – remote sensing – for diagnosing the multi-faceted and complex vulnerability of a city: • Which remotely sensed data sets are appropriate analyzing vulnerability in highly complex urban landscapes? • What capabilities and limitations does urban remote sensing have regarding mapping, analysis and assessment of risks and vulnerability? • How can interdisciplinary approaches extend the applicability of earth observation

A Novel Technique Based on the Combination of Labeled Co-Occurrence Matrix and Variogram for the Detection of Built-up Areas in High-Resolution SAR Images

Interests in synthetic aperture radar (SAR) data analysis is driven by the constantly increased spatial resolutions of the acquired images, where the geometries of scene objects can be better defined than in lower resolution data. This paper addresses the problem of the built-up areas extraction in high-resolution (HR) SAR images, which can provide a wealth of information to characterize urban environments. Strong backscattering behavior is one of the distinct characteristics of built-up areas in a SAR image. However, in practical applications, only a small portion of pixels characterizing the built-up areas appears bright. Thus, specific texture measures should be considered for identifying these areas. This paper presents a novel texture measure by combining the proposed labeled co-occurrence matrix technique with the specific spatial variability structure of the considered land-cover type in the fuzzy set theory. The spatial variability is analyzed by means of variogram, which reflects the spatial correlation or non-similarity associated with a particular terrain surface. The derived parameters from the variograms are used to establish fuzzy functions to characterize the built-up class and non built-up class, separately. The proposed technique was tested on TerraSAR-X images acquired of Nanjing (China) and Barcelona (Spain), and on a COSMO-SkyMed image acquired of Hangzhou (China). The obtained classification accuracies point out the effectiveness of the proposed technique in identifying and detecting built-up areas

Mapping Local Climate Zones for a Worldwide Database of the Form and Function of Cities

Progress in urban climate science is severely restricted by the lack of useful information that describes aspects of the form and function of cities at a detailed spatial resolution. To overcome this shortcoming we are initiating an international effort to develop the World Urban Database and Access Portal Tools (WUDAPT) to gather and disseminate this information in a consistent manner for urban areas worldwide. The first step in developing WUDAPT is a description of cities based on the Local Climate Zone (LCZ) scheme, which classifies natural and urban landscapes into categories based on climate-relevant surface properties. This methodology provides a culturally-neutral framework for collecting information about the internal physical structure of cities. Moreover, studies have shown that remote sensing data can be used for supervised LCZ mapping. Mapping of LCZs is complicated because similar LCZs in different regions have dissimilar spectral properties due to differences in vegetation, building materials and other variations in cultural and physical environmental factors. The WUDAPT protocol developed here provides an easy to understand workflow; uses freely available data and software; and can be applied by someone without specialist knowledge in spatial analysis or urban climate science. The paper also provides an example use of the WUDAPT project results