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Logistic Regression A Self Learning Text

Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods

Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance

Evaluation of vertical accuracy of open source Digital Elevation Model (DEM)

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method.

Prediction of possible landslide areas is the first stage of landslide hazard mitigation efforts and is also crucial for suitable site selection. Several statistical and machine learning methodologies have been applied for the production of landslide susceptibility maps. However, the performance assessment of such methods have conventionally been carried out by utilizing existing landslide inventories. The purpose of this study is to investigate the performances of landslide susceptibility maps produced with three different machine learning algorithms, i.e., random forest, artificial neural network, and logistic regression, in a recently constructed and activated dam reservoir and assess the external quality of each map by using pre- and post-event photogrammetric datasets. The methodology introduced here was applied using digital surface models generated from aerial photogrammetric flight data acquired before and after the dam construction. Aerial photogrammetric images acquired in 2012 and 2018 (after the dam was filled) were used to produce digital terrain models and orthophotos. The 2012 dataset was used for producing the landslide susceptibility maps and the results were evaluated by comparing the Euclidian distances between the two surface models. The results show that the random forest method outperforms the other two for predicting the future landslides.

A Novel Performance Assessment Approach Using Photogrammetric Techniques for Landslide Susceptibility Mapping with Logistic Regression, ANN and Random Forest.

The success rate in landslide susceptibility mapping efforts increased with the advancements in machine learning algorithms and the availability of geospatial data with high spatial and temporal resolutions. Existing data-driven susceptibility mapping models are not globally applicable due to the high variability of landslide conditioning parameters and the limitations in the availability of up-to-date and accurate data. Among numerous applications, landslide susceptibility maps are essential for site selection and health monitoring of engineering structures, such as dams, for increasing their lifetime and to prevent from disastrous events caused by the damages. In this study, landslide susceptibility mapping performance of XGBoost algorithm was evaluated in a landslide-prone area in the upper basin of Ataturk Dam, which is a prime investment located in the southeast of Turkey. The study area has a size of 2718.7 km2 with an elevation difference of ca. 2000 m and contains 27 lithological units. EU-DEM v1.1 from the Copernicus Programme was used to derive the geomorphological features. High classification accuracy with area under curve value of 0.96 could be obtained from the XGBoost algorithm. According to the results, the main factors controlling the landslides in the study area are the lithology, altitude and topographic wetness index.

A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey

Several scientific processes benefit from Citizen Science (CitSci) and VGI (Volunteered Geographical Information) with the help of mobile and geospatial technologies. Studies on landslides can also take advantage of these approaches to a great extent. However, the quality of the collected data by both approaches is often questionable, and automated procedures to check the quality are needed for this purpose. In the present study, a convolutional neural network (CNN) architecture is proposed to validate landslide photos collected by citizens or nonexperts and integrated into a mobile- and web-based GIS environment designed specifically for a landslide CitSci project. The VGG16 has been used as the base model since it allows finetuning, and high performance could be achieved by selecting the best hyper-parameters. Although the training dataset was small, the proposed CNN architecture was found to be effective as it could identify the landslide photos with 94% precision. The accuracy of the results is sufficient for purpose and could even be improved further using a larger amount of training data, which is expected to be obtained with the help of volunteers.

A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality

High-resolution satellite images (HRSI) at sub-5 m footprint are becoming increasingly available. A set of algorithms for processing of HRSI has been developed at the Institute of Geodesy and Photogrammetry (IGP), ETH Zurich and realised in a software suite called Satellite Image Precision Processing (SAT-PP). The software has been used for the processing of a number of high resolution satellite sensors, such as IKONOS, QuickBird, SPOT 5 HRS/HRG, Cartosat-1 and ALOS PRISM. PRISM is a panchromatic radiometer carried on board the Japanese ALOS satellite. It has three optical systems for forward, nadir and backward view with 2·5 m ground sample distance (GSD). The photogrammetric processing of PRISM imagery has special requirements owing to the linear array CCD sensor structure and special characteristics of the interior geometry and exterior orientation. As a member of the ALOS calibration/validation team, new algorithms for geometric processing of the PRISM images have been implemented at the IGP, in particular for the interior orientation and self-calibration. The physical sensor model in SAT-PP is refined according to the multiple camera heads of the sensor. The rigorous model for PRISM is based on a modified bundle adjustment with the possibility of using two different trajectory models. The self-calibration is introduced into the adjustment to model the systematic errors of the sensor and the system as a whole. The methods of georeferencing and digital surface model (DSM) generation were tested using the PRISM data-sets acquired over five different testfields. The rigorous sensor model performed well and resulted in sub-pixel accuracy for point positioning in all testfields. The self-calibration model has been tested in two different phases of the project separately. In the initial phase, where interior orientation data was not available, the use of the self-calibration was essential to achieve good accuracy. However, in the later phase the relative positions of the CCD chip detectors on the focal plane were provided by the Japan Aerospace Exploration Agency (JAXA) and the improvements by self-calibration became less significant. A detailed analysis of the DSM generation is presented in another publication.

Resume

Les images-satellite haute resolution (HRSI) de pixel au sol inferieure a 5 m sont disponibles de plus en plus facilement. Un ensemble d’algorithmes de traitements d’HRSI a ete developpea l’Institut de Geodesie et de Photogrammetrie (IGP) de l’ETH Zurich et integre dans une suite logicielle nommee SAT-PP (Satellite Image Precision Processing). Ce logiciel a ete utilise pour traiter les donnees d’un grand nombre de capteurs comme IKONOS, QuickBird, SPOT 5 HRS/HRG, Cartosat-1 et ALOS PRISM. PRISM est un radiometre panchromatique embarque sur le satellite japonais ALOS. Il possede trois capteurs optiques permettant des vues avant, arriere et nadirales avec une resolution de 2,5 m. Le traitement photogrammetrique des images PRISM a des contraintes specifiques liees a la structure par barrette du capteur CCD et aux caracteristiques particulieres de sa geometrie interne et de son orientation externe. En tant que Membre de l’Equipe de Calibration/Validation d’ALOS, l’IGP a implemente de nouveaux algorithmes de traitements geometriques des images PRISM, en particulier sur l’orientation interne et l’auto-etalonnage. Le modele physique du capteur present dans SAT-PP est affine selon les multiples tetes de camera du capteur. Le modele rigoureux de PRISM est fonde sur une compensation par faisceaux modifiee avec la possibilite d’utiliser deux modeles de trajectoire differents. L’auto-etalonnage est introduit dans la compensation pour modeliser les erreurs systematiques du capteur et du systeme dans son ensemble. Les methodes de georeferencement et de generation de MNS sont testees en utilisant des jeux de donnees PRISM acquis sur cinq differentes zones-test. Le modele rigoureux du capteur convient parfaitement et fournit une precision sub-pixellaire sur la localisation des points dans toutes les zones-test. Le modele d’auto-etalonnage a ete teste dans les deux phases successives du projet. Lors de la phase initiale, les donnees d’orientation interne n’etant pas disponibles, le recours a l’auto-etalonnage etait essentiel pour obtenir une bonne precision. Cependant, lors de la derniere phase, les positions relatives dans le plan focal des detecteurs a barrette CCD ont ete fournies par JAXA et les ameliorations provenant de l’auto-etalonnage sont devenues moins notables. Une analyse detaillee de la generation du MNS est presentee dans une autre publication.

Zusammenfassung

Hochauflosende, stereofahige Satellitensensoren spielen eine zunehmend grossere Rolle in der Geomatik. Viele Aufgaben, die bisher der Luftbildauswertung vorbehalten waren, konnen nun mit diesen Satellitenbildern erledigt werden, neue Aufgaben kommen dazu. Wir haben am Institut fur Geodasie und Photogrammetrie der ETH Zurich, Schweiz das Softwarepaket SAT-PP (Satellite Image Precision Processing) entwickelt, welches die wichtigsten Funktionen zur Auswertung von stereofahigen Satellitenbildern enthalt. In diesem Beitrag berichten wir uber Resultate zur Georeferenzierung (Orientierung) dieser Bilder. Wir haben diverse Sensormodelle implementiert (Physische Modelle, Funktionen Rationaler Polynome und andere, einfachere nichtparametrische Funktionen, gemischte Modelle). Insbesondere verweisen wir auf die Implementierung der Methode der Selbstkalibrierung, welche es uns erlaubt, auch strenge Sensormodelle ohne detaillierte Kenntnisse der Inneren und Aeusseren Orientierung erfolgreich aufzusetzen. In fruheren Publikationen hatten wir uber validierte Ergebnisse der Georeferenzierung von SPOT 5, IKONOS und QuickBird Bildern berichtet. Hier konzentrieren wir uns auf den ALOS PRISM Sensor. Als Mitglieder des ALOS Kalibrierungs- und Validierungsteams hatten und haben wir Zugang zu diversen Datensatzen. Wir prasentieren Ergebnisse aus 5 Testfeldern: Piemont, Italien; Saitama und Okazaki, Japan; Bern/Thun und Zurich/Winterthur, Schweiz. Die Ergebnisse zeigen konsistent Genauigkeiten der Georeferenzierung im Subpixelbereich in Lage und Hohe. Es fallt auf, dass die Hohengenaugkeit fast in allen Fallen besser als die Lagegenauigkeit ist. Dies liegt an der hoheren planimetrischen Definitionsunsicherheit der Pass- und Kontrollpunkte. Als weiteres Ergebnis konnen wir festhalten, dass sehr einfache Trajektorienmodelle—in unserem Falle die DGR (Direct Georeferencing with pushbroom model and stochastic exterior orientation elements) mit nur 9 Parametern der Aeusseren Orientierung pro Kamera—genugen, um sehr gute Ergebnisse zu erzielen. Die Anzahl benotigter Passpunkte richtet sich primar nach der Grosse der a priori Gewichte der beobachteten Orientierungsgrossen, aber auch nach der Art und Anzahl zusatzlicher Parameter zur Selbstkalibrierung. Wir konnten zeigen, dass abhangig von den stochastischen a priori Annahmen, mit nur 2–5 Passpunkten die wichtigsten Parameter bestimmt werden konnen.

Resumen

La disponibilidad de imagenes de alta resolucion con una resolucion inferior a 5 m es cada vez mas comun. En el Institut fur Geodasie und Photogrammetrie (IGP), ETH Zurich, se ha desarrollado un conjunto de algoritmos para procesar estas imagenes y se han integrado en la libreria de programas SAT-PP (Satellite Image Precision Processing). Dichos programas ya habian sido utilizados en el procesamiento de un cierto numero de imagenes de varios sensores de alta resolucion tales como IKONOS, QuickBird, SPOT 5 HRS/HRG, Cartosat-1 y ALOS PRISM. PRISM es un radiometro pancromatico instalado en el satelite japones ALOS. Tiene tres sistemas opticos que proporcionan vistas anterior, nadir y posterior con una resolucion en el terreno de 2,5 m. El procesamiento de las imagenes PRISM tiene unos requerimientos especiales a causa de la estructura del sensor CCD de matriz lineal y de las caracteristicas especiales de la geometria interna y de la orientacion externa. En tanto que miembros del equipo de calibracion y validacion de ALOS, el IGP ha desarrollado nuevos algoritmos de procesamiento geometrico de las imagenes PRISM, en particular los relativos a la orientacion interna y la autocalibracion. El modelo fisico del sensor en SAT-PP se afina en funcion de los multiples cabezales del sensor. El modelo riguroso de PRISM se basa en un ajuste de haces modificado que permite la opcion de utilizar dos modelos de trayectoria. La autocalibracion se introduce en el ajuste para modelar los errores sistematicos del sensor y del sistema considerado como un todo. Los metodos de georreferenciacion y generacion de modelos digitales de superficie se comprobaron usando los datos PRISM de cinco campos de ensayo diferentes. El modelo riguroso del sensor funciona adecuadamente lo que permitio determinar puntos con exactitud subpixel en todos los campos de ensayo. El modelo de autocalibracion ha sido comprobado por separado en dos fases diferentes del proyecto. En la fase inicial, en la que no se disponia de datos de orientacion interna, la autocalibracion era esencial para lograr una buena exactitud. En la ultima fase, JAXA aporto informacion sobre la posicion relativa de los fotodetectores del CCD en el plano focal, con lo que las mejoras de la autocalibracion fueron menos significativas. El analisis detallado del calculo del modelo digital de superficie se presenta en otra publicacion.

Orientation and self‐calibration of ALOS PRISM imagery

Urban areas may be affected by multiple hazards, and integrated hazard susceptibility maps are needed for suitable site selection and planning. Furthermore, geological–geotechnical parameters, construction costs, and the spatial distribution of existing infrastructure should be taken into account for this purpose. Up-to-date land-use and land-cover (LULC) maps, as well as natural hazard susceptibility maps, can be frequently obtained from high-resolution satellite sensors. In this study, an integrated hazard susceptibility assessment was performed for a developing urban settlement (Mamak District of Ankara City, Turkey) considering landslide and flood potential. The flood susceptibility map of Ankara City was produced in a previous study using modified analytical hierarchical process (M-AHP) approach. The landslide susceptibility map was produced using the logistic regression technique in this study. Sentinel-2 images were employed for generating LULC data with the random forest classification method. Topographical derivatives obtained from a high-resolution digital elevation model and lithological parameters were employed for the production of landslide susceptibility maps. For the integrated hazard susceptibility assessment, the Mamdani fuzzy algorithm was considered, and the results are discussed in the present study. The results demonstrate that multi-hazard susceptibility assessment maps for urban planning can be obtained by combining a set of expert-based and ensemble learning methods.

Sultan Kocaman

Papers

A Novel Performance Assessment Approach Using Photogrammetric Techniques for Landslide Susceptibility Mapping with Logistic Regression, ANN and Random Forest.

A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey

A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality

Orientation and self‐calibration of ALOS PRISM imagery

Use of Mamdani Fuzzy Algorithm for Multi-Hazard Susceptibility Assessment in a Developing Urban Settlement (Mamak, Ankara, Turkey)