Leveraging machine learning to extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): a case study in forest-type mapping

Ontology-driven Geographic Object-Based Image Analysis (O-GEOBIA) contributes to the identification of meaningful objects. In fusing data from multiple sensors, the number of feature variables is increased and object identification becomes a challenging task. We propose a methodological contribution that extends feature variable characterisation. This method is illustrated with a case study in forest-type mapping in Tasmania, Australia. Satellite images, airborne LiDAR (Light Detection and Ranging) and expert photo-interpretation data are fused for feature extraction and classification. Two machine learning algorithms, Random Forest and Boruta, are used to identify important and relevant feature variables. A variogram is used to describe textural and spatial features. Different variogram features are used as input for rule-based classifications. The rule-based classifications employ (i) spectral features, (ii) vegetation indices, (iii) LiDAR, and (iv) variogram features, and resulted in overall classification accuracies of 77.06%, 78.90%, 73.39% and 77.06% respectively. Following data fusion, the use of combined feature variables resulted in a higher classification accuracy (81.65%). Using relevant features extracted from the Boruta algorithm, the classification accuracy is further improved (82.57%). The results demonstrate that the use of relevant variogram features together with spectral and LiDAR features resulted in improved classification accuracy

Landslide detection using multi-scale image segmentation and different machine learning models in the higher Himalayas

Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining landslide susceptibility and risk. Satellite data have been widely used for such investigations—next to data from airborne or unmanned aerial vehicle (UAV)-borne campaigns and Digital Elevation Models (DEMs). We have developed a methodology that incorporates object-based image analysis (OBIA) with three machine learning (ML) methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection. We identified the optimal scale parameters (SP) and used them for multi-scale segmentation and further analysis. We evaluated the resulting objects using the object pureness index (OPI), object matching index (OMI), and object fitness index (OFI) measures. We then applied two different methods to optimize the landslide detection task: (a) an ensemble method of stacking that combines the different ML methods for improving the performance, and (b) Dempster–Shafer theory (DST), to combine the multi-scale segmentation and classification results. Through the combination of three ML methods and the multi-scale approach, the framework enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal. PlanetScope optical satellite images and a DEM were used, along with the derived landslide conditioning factors. Different accuracy assessment measures were used to compare the results against a field-based landslide inventory. All ML methods yielded the highest overall accuracies ranging from 83.3% to 87.2% when using objects with the optimal SP compared to other SPs. However, applying DST to combine the multi-scale results of each ML method significantly increased the overall accuracies to almost 90%. Overall, the integration of OBIA with ML methods resulted in appropriate landslide detections, but using the optimal SP and ML method is crucial for success

Mapeamento de fragmentos florestais para elaboração de planos municipais da Mata Atlântica

Trabalho de Conclusão de Curso, apresentado para obtenção do grau de Engenheiro Agrimensor no curso de Engenharia de Agrimensura da Universidade do Extremo Sul Catarinense, UNESC.Os dados de sensoriamento remoto ganharam nas últimas décadas espaço de destaque no monitoramento de recursos naturais, sendo um dos principais produtos utilizados para atender à crescente demanda por mapas temáticos. Uma importante aplicação é o uso de imagens orbitais e técnicas de processamento digital para o mapeamento e o monitoramento da vegetação, principalmente quando sofre com as ações antrópicas. Para tentar reduzir essa pressão no bioma Mata Atlântica, foi criada legislação específica para sua proteção, possibilitando que os municípios atuem ativamente na sua conservação, tendo como ferramenta o Plano Municipal de Conservação e Recuperação da Mata Atlântica (PMMA). Neste contexto, o trabalho teve como principal objetivo desenvolver uma metodologia para mapeamento de fragmentos florestais diferenciando vegetação secundária e cultivos silvícolas a fim de auxiliar na elaboração de Planos Municipais da Mata Atlântica. Foram utilizadas imagens do sistema sensor Sentinel-2 para o município de Orleans, além de técnicas de análise de imagens baseada em objetos geográficos, em que foram testados os índices espectrais NDVI e CO2Flux. A análise da acurácia dos produtos gerados pelos dois índices apontou que o CO2Flux foi mais eficiente no mapeamento da vegetação, apresentando menos conflitos, melhor definição dos objetos além de coeficientes de concordância mais elevados. O teste de hipótese apontou que a diferença entre os índices é extremamente significativa. O mapeamento da vegetação apontou que Orleans possui 58,78% do seu território coberto por vegetação, e que embora fragmentada, mais de 91% da vegetação se encontra em manchas com área superior a 100 ha. Dentre as formações fitoecológicas encontradas no município, a Alto-Montana apresenta maior cobertura de vegetação, enquanto a Submontana tem a maior redução da cobertura original e a maior fragmentação. Verificou-se também que as áreas protegidas desempenham importante papel na conservação da cobertura florestal, tendo estas mais de 72% da área coberta por vegetação, representando mais de 55% da vegetação do município de Orleans. Foi avaliada a escala dos produtos cartográficos gerados, sendo observado que estes são compatíveis com a escala 1:25.000, atendendo o exigido para o PMMA

Using Machine Learning in Forestry

Advanced technology has increased demands and needs for innovative approaches to apply traditional methods more economically, effectively, fast and easily in forestry, as in other disciplines. Especially recently emerging terms such as forestry informatics, precision forestry, smart forestry, Forestry 4.0, climate-intelligent forestry, digital forestry and forestry big data have started to take place on the agenda of the forestry discipline. As a result, significant increases are observed in the number of academic studies in which modern approaches such as machine learning and recently emerged automatic machine learning (AutoML) are integrated into decision-making processes in forestry. This study aims to increase further the comprehensibility of machine learning algorithms in the Turkish language, to make them widespread, and be considered a resource for researchers interested in their use in forestry. Thus, it was aimed to bring a review article to the national literature that reveals both how machine learning has been used in various forestry activities from the past to the present and its potential for use in the future

Image-based Semantic Segmentation of Large-scale Terrestrial Laser Scanning Point Clouds

Large-scale point cloud data acquired using terrestrial laser scanning (TLS) often need to be semantically segmented to support many applications. To this end, various three-dimensional (3D) methods and two-dimensional (i.e., image-based) methods have been developed. For large-scale point cloud data, 3D methods often require extensive computational effort. In contrast, image-based methods are favourable from the perspective of computational efficiency. However, the semantic segmentation accuracy achieved by existing image-based methods is significantly lower than that achieved by 3D methods. On this basis, the aim of this PhD thesis is to improve the accuracy of image-based semantic segmentation methods for TLS point cloud data while maintaining its relatively high efficiency. In this thesis, the optimal combination of commonly used features was first found, and an efficient manual feature selection method was proposed. It was found that existing image-based methods are highly dependent on colour information and do not provide an effective means of representing and utilising geometric features of scenes in images. To address this problem, an image enhancement method was developed to reveal the local geometric features in images derived by the projection of point cloud coordinates. Subsequently, to better utilise neural network models that are pre-trained on three-channel (i.e., RGB) image datasets, a feature extraction method (LC-Net) and a feature selection method (OSTA) were developed to reduce the higher dimension of image-based features to three. Finally, a stacking-based semantic segmentation (SBSS) framework was developed to further improve segmentation accuracy. By integrating SBSS, the dimension-reduction method (i.e. OSTA) and locally enhanced geometric features, a mean Intersection over Union (mIoU) of 76.6% and an Overall Accuracy (OA) of 93.8% were achieved on the Semantic3D (Reduced-8) benchmark. This set the state-of-the-art (SOTA) for the semantic segmentation accuracy of image-based methods and is very close to the SOTA accuracy of 3D method (i.e., 77.8% mIoU and 94.3% OA). Meanwhile, the integrated method took less than 10% of the processing time (52.64s versus 563.6s) of the fastest SOTA 3D method