Image Segmentation Approaches Applied for the Earth's Surface

An analytical review of papers about remote sensing, as well as semantic segmentation and classification methods to process these data, is carried out. Approaches such as template matching-based methods,machine learning and neural networks, as well as the application of knowledge about the analyzed objects are considered. The features of vegetation indices usage for data segmentation by satellite images are considered.Advantages and disadvantages are noted. Recommendations operations for a more accurate classification of thedetected areas on the sequence are give

Automatic Segmentation of Land Cover in Satellite Images

Semantic segmentation problems such as landcover segmentation rely on large amounts of annotated images to excel. Without such data for target regions, transfer learning methods are widely used to incorporate knowledge from other areas and domains to improve performance. In this study, we analyze the performance of landcover segmentation models trained on low-resolution images with insufficient data for the targeted region or zoom level. In order to boost performance on target data, we experiment with models trained with unsupervised, semi-supervised, and supervised transfer learning approaches, including satellite images from public datasets and other unlabeled sources.According to experimental results, transfer learning improves segmentation performance by 3.4% MIoU (mean intersection over union) in rural regions and 12.9% MIoU in urban regions. We observed that transfer learning is more effective when two datasets share a comparable zoom level and are labeled with identical rules; otherwise, semi-supervised learning is more effective using unlabeled data. Pseudo labeling based unsupervised domain adaptation method improved building detection performance in urban cities. In addition, experiments showed that HRNet outperformed building segmentation approaches in multi-class segmentation

Segmentação semântica profunda para detecção de florestas plantadas de eucalipto no território brasileiro usando imagens Sentinel-2

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-graduação, 2021.As florestas plantadas de eucalipto possuem grande importância econômica para o Brasil e os extratos de eucalipto são utilizados em diversos setores da indústria mundial. As técnicas de sensoriamento remoto são de suprema importância para o estudo e o monitoramento destas áreas, além de ser uma ferramenta essencial para a determinação de planos de ação na economia e na política. O Deep Learning surge atualmente como uma alternativa de automatização e melhoria da eficiência das técnicas de machine learning. Este estudo tem como objetivo analisar o uso da segmentação semântica profunda na detecção de áreas de plantios de eucalipto por meio de imagens Sentinel-2, por ter observado a importância econômica o eucalipto para o desenvolvimento de estudos automatizados para monitoramento desta cultura. O foco deste trabalho é na comparação de seis arquiteturas de Deep Learning (U-net, DeepLabv3 +, FPN, MANet, PSPNet, LinkNet) com quatro codificadores (ResNet-101, ResNeXt-101, Efficient-net- b3 e Efficient-net-b7), usando 10 bandas espectrais, excluindo apenas as 3 bandas relacionadas à atmosfera. Mesmo que as diferenças não fossem grandes entre os diferentes modelos, descobrimos que o Efficient-net-b7 foi o melhor codificador entre todas as arquiteturas e o melhor modelo geral foi DeepLabv3 + com o codificador Efficient-net-b7, alcançando um IoU de 76,57. Além disso, comparamos o mapeamento de grandes imagens de satélite com a técnica de janela deslizante com pixels sobrepostos considerando seis diferentes valores de passada. Descobrimos que as janelas deslizantes com valores de passada mais baixos minimizaram significativamente os erros na borda do quadro, tanto visual quanto quantitativamente (métricas). A segmentação semântica permite uma distinção evidente entre a arborização e a vegetação natural, sendo rápida e eficiente para a análise da distribuição espacial das mudanças da arborização no Brasil. Técnicas mais assertivas na identificação do alvo por meio das imagens de satélite para alimentar as redes de Deep Learning poderão melhorar ainda mais a precisão das informações encontradas por estas redes.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).This research aims to analyze the use of deep semantic segmentation to detect eucalyptus afforestation areas using Sentinel-2 images. The study compared six architectures (U-net, DeepLabv3+, FPN, MANet, PSPNet, LinkNet) with four encoders (ResNet-101, ResNeXt-101, Efficient-net-b3, and Efficient-net-b7), using 10 spectral bands. Even though the differences were not large among the different models, we found that the Efficient-net-b7 was the best backbone among all architectures, and the best overall model was DeepLabv3+ with the Efficient-net-b7 backbone, achieving an IoU of 76.57. Moreover, we compared the mapping of large satellite images with the sliding window technique with overlapping pixels considering six stride values. We found that sliding windows with lower stride values significantly minimized errors in the frame edge both visually and quantitively (metrics). Semantic segmentation allows an evident distinction between the afforestation and the natural vegetation, being fast and efficient for spatial distribution analysis of afforestation changes in Brazil

Deep semantic segmentation of mangrove combining spatial, temporal and polarization from sentinel-1 time series in the Brazilian territory

Dissertação (mestrado) — Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-graduação, 2022.O uso de imagens de satélite para detecção de padrões com alto grau de precisão traz a possibilidade da realização de um monitoramento estratégico com foco na conservação da biodiversidade local. Técnicas de aprendizagem profunda por segmentação do objeto imagem seguem ganhando espaço em aplicações de processamento digital de imagens de satélite. Estas aplicações seguem atingindo resultados muito superior e sem relação aos métodos tradicionais de aprendizagem de máquina. Entretanto, poucos estudos têm aplicado o aprendizado profundo em áreas de manguezais. Além disso, ainda não foram desenvolvidas técnicas por séries temporais de imagens de radar. A presente pesquisa tem por objetivo: (a) desenvolver dados para a aprendizagem profunda de máquina - levando em consideração abordagens espaciais, temporais e dimensionais (polarizações radiométricas); (b) validar modelos U-net com três diferentes arquiteturas (ResNet-101, VGG16 e Efficient-net-B7); (c) comparar a capacidade de detecção em imagens Sentinel-1 utilizando as polarizações VV, VH e duplas polarizações (VV+VH); (d) e quantificar o número de imagens temporais para melhor detecção dos objetos. A pesquisa utiliza séries temporais Sentinel-1 anual entre períodos de 2017 a 2020. As amostras de treinamento e validação dos dados foram geradas a partir de interpretação manual de imagem de satélite. Estes dados resultaram em 2886 imagens com dimensão espacial de 128x128 pixels. 2136 destas imagens foram utilizadas para treinamento, 450 para validação e 300 para testagem. Acombinação de polarizações (VV+VH), omodelo U-net com arquitetura Efficient-net-B7 e limiar de0,75 (97,35 de acurácia, 85,77 de precisão, 84,96 de recall, 85,36 de F-score e 74,46 de IoU)obtiveram os melhores resultados. 5 passadas (8, 16, 32, 64, 128 pixels) foram aplicadas nas janelas deslizantes. O melhor resultado obtido foi com 8 pixels de passada. O método desenvolvido é adequado ao monitoramento dos padrões temporais de manguezais e fornece subsídios às políticas de conservação destes ecossistemas.The automatic and accurate detection of mangroves from remote sensing data is essential to assist in conservation strategies and decision-making that minimize possible environmental damage, especially for the Brazilian coast with continental dimensions. In this context, segmentation techniques using deep learning are powerful tools with successful applications in several areas of science, achieving results superior to traditional machine learning methods. However, few studies used deep learning for mangrove areas, and none considered time series of radar images. The present research has the following objectives: (a) development of a mangrove dataset for deep learning in the Southeast region of Brazil considering the spatial, temporal, and polarization dimensions; (b) evaluation of U-net architecture models with three backbones different (ResNet -101, VGG16, and Efficient-net-B7); (c) compare the detection capability of Sentinel-1 images using the following VV-only, VH-only, and VV+VH polarizations; and (d) evaluate the number of temporal images for the best detection of targets (29, 15, 8, 4 images), in the case of using both polarizations the number of images doubles. This research uses the annual Sentinel-1 time series for the period 2017-2020. Data labeling used manual interpretation, resulting in 2,886 images with spatial dimensions of 128x128 pixels and their respective annotations (2,136 for training, 450 for validation, and 300 for testing). The best result considered both polarizations (VV+VH), the maximum number of timeseries images (29 VV and 29 VH), U-net with the Efficient-net-B7 backbone, and a threshold of 0.75 (97.35 accuracy, 85.77 precision, 84.96 recall, 85.36 F-score, and 74.46 IoU). The entire image classification used a sliding window approach considering five stride values (8, 16, 32, 64, 128 pixels), where the best result was with 8 pixels. The present method is suitable for monitoring mangrove patterns over time, providing subsidies for conserving these ecosystems

Towards a scalable and transferable approach to map deprived areas using Sentinel-2 images and machine learning

African cities are growing rapidly and more than half of their populations live in deprived areas. Local stakeholders urgently need accurate, granular, and routine maps to plan, upgrade, and monitor dynamic neighborhood-level changes. Satellite imagery provides a promising solution for consistent, accurate high-resolution maps globally. However, most studies use very high spatial resolution images, which often cover only small areas and are cost prohibitive. Additionally, model transferability to new cities remains uncertain. This study proposes a scalable and transferable approach to routinely map deprived areas using free, Sentinel-2 images. The models were trained and tested on three cities: Lagos (Nigeria), Accra (Ghana), and Nairobi (Kenya). Contextual features were extracted at 10 m spatial resolution and aggregated to a 100 m grid. Four machine learning algorithms were evaluated, including multi-layer perceptron (MLP), Random Forest, Logistic Regression, and Extreme Gradient Boosting (XGBoost). The scalability of model performance was examined using patches of the different deprived types identified through visual image interpretation. The study also tested the ability of models to map deprived areas of different types across cities. Results indicate that deprived areas have heterogeneous local characteristics that affect large area mapping. The top 25 features for each city show that models are sensitive to the spatial structures of deprived area types. While models performed well on individual cities with XGBoost and MLP achieving an F1 scores of over 80%, the generalized model proves to be more beneficial for modeling multiple cities. This approach offers a promising solution for scaling routine, accurate maps of deprived areas to hundreds of cities that currently lack any such map, supporting local stakeholders to plan, implement, and monitor geotargeted interventions

Contaminación ambiental y Geociencias. Una revisión bibliográfica

Trabajo de Fin de Máster del Máster en Geotecnologías cartográficas en ingeniería y arquitectura, curso...Con el presente trabajo, se realizará una revisión bibliográfica que permita examinar la información disponible sobre las aplicaciones y herramientas en el campo de la Geociencias las cuales aporten soluciones en la detección, prevención, seguimiento y/o modelación de eventos y agentes contaminantes del medio ambiente. Para ello se abordará una búsqueda sistémica de los últimos cinco años, que contenga información actualiza novedosa y que describa los avances más importantes durante este periodo. De la misma manera se pretende establecer la incidencia y el manejo que tienen estas herramientas en la extracción de datos fundamentales para identificar agentes contaminantes, zonas de alteración ambiental y consecuencias en los ecosistemas a fin de encontrar acciones que mitiguen y/o adapten prácticas que mejoren las condiciones del hábitat del ser humano. La estructura de este documento está conformada de la siguiente manera; En primer lugar, aparece la metodología, donde se presentan los criterios de selección y búsqueda del material bibliográfico, describiendo las bases de datos utilizadas, las palabras claves, los criterios de selección inclusión y exclusión, terminando con el diagrama de flujo que describe los pasos mencionados. En segundo lugar, se describen los resultados de la búsqueda y selección de los artículos, así como su clasificación, que para el presente trabajo se determinó así, los que aplican a grandes áreas como; Sistemas de Información Geográfica (SIG), Sensores Remotos (SR), 7 Big Data, Machine Learning (ML) y Sensores Web Geoespaciales (SGW); y los que se aplican en áreas pequeñas o casos puntuales como; Biosensores, Nariz electrónica, Ciencia ciudadana y vehículos aéreos no tripulados comúnmente conocido como drones. En seguida, se presenta el análisis y los argumentos de cada uno de los elementos bibliográficos seleccionados, así como las apreciaciones de los desarrollos tecnológicos de la Geociencias y su incidencia e importancia en el campo de los estudios de contaminación ambiental. Finalmente se presentan las conclusiones y recomendaciones del presente trabajo. Es importante mencionar que, en el proceso de análisis y selección de la información, el autor enfrenta una continua toma de decisiones las cuales constituyen en sí mismas la selección y exclusión de información que imprime un curso y dirección de argumento personal

Inteligência artificial e sistemas de irrigação por pivô central : desenvolvimento de estratégias e técnicas para o aprimoramento do mapeamento automático

Tese (doutorado) — Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2022.A irrigação é o principal responsável pelo aumento da produtividade dos cultivos. Os sistemas de irrigação por pivô central (SIPC) são líderes em irrigação mecanizada no Brasil, com expressivo crescimento nas últimas décadas e projeção de aumento de mais de 134% de área até 2040. O método mais utilizado para identificação de SIPC é baseado na interpretação visual e mapeamento manual das feições circulares, tornando a tarefa demorada e trabalhosa. Nesse contexto, métodos baseados em Deep Learning (DL) apresentam grande potencial na classificação de imagens de sensoriamento remoto, utilizando Convolutional Neural Networks (CNN’s). O uso de DL provoca uma revolução na classificação de imagens, superando métodos tradicionais e alcançando maior precisão e eficiência, permitindo monitoramento regional e contínuo com baixo custo e agilidade. Essa pesquisa teve como objetivo aplicação de técnicas de DL utilizando algoritmos baseados em CNN’s para identificação de SIPC em imagens de sensoriamento remoto. O presente trabalho foi dividido em três capítulos principais: (a) identificação de SIPC em imagens Landsat-8/OLI, utilizando segmentação semântica com três algoritmos de CNN (U-Net, Deep ResUnet e SharpMask); (b) detecção de SIPC usando segmentação de instâncias de imagens multitemporais Sentinel-1/SAR (duas polarizações, VV e VH) utilizando o algoritmo Mask-RCNN, com o backbone ResNeXt-101-32x8d; e (c) detecção de SIPC utilizando imagens multitemporais Sentinel-2/MSI com diferentes percentuais de nuvens e segmentação de instâncias utilizando Mask-RCNN, com o backbone ResNext-101. As etapas metodológicas foram distintas entre os capítulos e todas apresentaram altos valores de métricas e grande capacidade de detecção de SIPC. As classificações utilizando imagens Landsat-8/OLI, e os algoritmos U-Net, Depp ResUnet e SharpMask tiveram respectivamente 0,96, 0,95 e 0,92 de coeficientes Kappa. As classificações usando imagens Sentinel-1/SAR apresentaram melhores métricas na combinação das duas polarizações VV+VH (75%AP, 91%AP50 e 86%AP75). A classificação de imagens Sentinel-2/MSI com nuvens apresentou métricas no conjunto de 6 imagens sem nuvens (80%AP e 93%AP50) bem próximas aos valores do conjunto de imagens com cenário extremo de nuvens (74%AP e 88%AP50), demonstrando que a utilização de imagens multitemporais, aumenta o poder preditivo no aprendizado. Uma contribuição significativa da pesquisa foi a proposição de reconstrução de imagens de grandes áreas, utilizando o algoritmo de janela deslizante, permitindo várias sobreposições de imagens classificadas e uma melhor estimativa de pivô por pixel. O presente estudo possibilitou o estabelecimento de metodologia adequada para detecção automática de pivô central utilizando três tipos diferentes de imagens de sensoriamento remoto, que estão disponíveis gratuitamente, além de um banco de dados com vetores de SIPC no Brasil Central.Irrigation is primarily responsible for increasing crop productivity. Center pivot irrigation systems (CPIS) are leaders in mechanized irrigation in Brazil, with significant growth in recent decades and a projected increase of more than 134% in area by 2040. The most used method for identifying CPIS is based on the interpretation visual and manual mapping of circular features, making the task time-consuming and laborious. In this context, methods based on Deep Learning (DL) have great potential in the classification of remote sensing images, using Convolutional Neural Networks (CNN's). The use of Deep Learning causes a revolution in image classification, surpassing traditional methods and achieving greater precision and efficiency, allowing regional and continuous monitoring with low cost and agility. This research aimed to apply DL techniques using algorithms based on CNN's to identify CIPS in remote sensing images. The present work was divided into three main chapters: (a) identification of CIPS in Landsat-8/OLI images, using semantic segmentation with three CNN algorithms (UNet, Deep ResUnet and SharpMask); (b) CPIS detection using Sentinel-1/SAR multitemporal image instance segmentation (two polarizations, VV and VH) using the Mask-RCNN algorithm, with the ResNeXt-101-32x8d backbone; and (c) SIPC detection using Sentinel2/MSI multitemporal images with different percentages of clouds and instance segmentation using Mask-RCNN, with the ResNext-101 backbone. The methodological steps were different between the chapters and all presented high metric values and great CPIS detection capacity. The classifications using Landsat-8/OLI images, and the U-Net, Depp ResUnet and SharpMask algorithms had respectively 0.96, 0.95 and 0.92 of Kappa coefficients. Classifications using Sentinel-1/SAR images showed better metrics in the combination of the two VV+VH polarizations (75%AP, 91%AP50 and 86%AP75). The classification of Sentinel-2/MSI images with clouds presented metrics in the set of 6 images without clouds (80%AP and 93%AP50) very close to the values of the set of images with extreme cloud scenario (74%AP and 88%AP50), demonstrating that the use of multitemporal images increases the predictive power in learning. A significant contribution of the research was the proposition of reconstruction of images of large areas, using the sliding window algorithm, allowing several overlaps of classified images and a better estimation of pivot per pixel. The present study made it possible to establish an adequate methodology for automatic center pivot detection using three different types of remote sensing images, which are freely available, in addition to a database with CPIS vectors in Central Brazil

Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network

Urban building segmentation is a prevalent research domain for very high resolution (VHR) remote sensing; however, various appearances and complicated background of VHR remote sensing imagery make accurate semantic segmentation of urban buildings a challenge in relevant applications. Following the basic architecture of U-Net, an end-to-end deep convolutional neural network (denoted as DeepResUnet) was proposed, which can effectively perform urban building segmentation at pixel scale from VHR imagery and generate accurate segmentation results. The method contains two sub-networks: One is a cascade down-sampling network for extracting feature maps of buildings from the VHR image, and the other is an up-sampling network for reconstructing those extracted feature maps back to the same size of the input VHR image. The deep residual learning approach was adopted to facilitate training in order to alleviate the degradation problem that often occurred in the model training process. The proposed DeepResUnet was tested with aerial images with a spatial resolution of 0.075 m and was compared in performance under the exact same conditions with six other state-of-the-art networks—FCN-8s, SegNet, DeconvNet, U-Net, ResUNet and DeepUNet. Results of extensive experiments indicated that the proposed DeepResUnet outperformed the other six existing networks in semantic segmentation of urban buildings in terms of visual and quantitative evaluation, especially in labeling irregular-shape and small-size buildings with higher accuracy and entirety. Compared with the U-Net, the F1 score, Kappa coefficient and overall accuracy of DeepResUnet were improved by 3.52%, 4.67% and 1.72%, respectively. Moreover, the proposed DeepResUnet required much fewer parameters than the U-Net, highlighting its significant improvement among U-Net applications. Nevertheless, the inference time of DeepResUnet is slightly longer than that of the U-Net, which is subject to further improvement

Deep Learning Based Exposure Analysis of LandslideProne Areas in Medellín, Colombia

In the last century, Medellín grew into one of Colombia's largest cities. Today, the city continues to grow primarily due to the influx of internally displaced people (IDP’s), who have been forced to leave their homes at the country side due to natural disasters or drug-related violence. Since the internally displaced are mostly lowincome farmers and peasants, they are migrating to the larger cities in search of greater security and jobs. In Medellín, the new residents mostly settle informally on the steep slopes to the east and west of the city. Due to limited space and steep topography, such settlements are often built in areas with medium and high probability of landslides. However, not only free land area within the municipal boundaries are exploited by the build-up of new settlements, but also free land beyond the border of the municipality, which causes the city to grow into the rural area. The study therefore seeks to find out how many residents are prone to potential landslide activity in the context of the pattern of migration. To analyze the exposure, the population is disaggregated down to the individual building block level. Such an approach requires precise building footprints to locate the population in relation to landslide-prone areas. Although the city has a cadaster including building footprints, it is more imprecise and incomplete towards the outskirts of the city, where landslide susceptibility is most pronounced. The incompleteness is due to the high population dynamics, which makes it quite difficult to maintain an up-to-date cadaster. But since Medellín's geospatial data service provides an orthophoto from 2019, a deep learning-based building extraction is applied to generate a more comprehensive building footprint dataset. This will be the main data source for the exposure analysis. The respective deep learning architecture is a U-Net has been refined with the EfficientNetB2 as a backbone and eventually fine-tuned. It could show very accurate results, while still facing some challenges, like the field-of.view of the image tiles, that is sometimes too small for the vast rooftop landscapes, which leads to misclassifications. The exposure analysis of population exposed to landslide hazard could prove the importance of having a more up-to-date data basis. While the number of residents living in formal settlements is not to different from the cadaster and the deeplearning derived building footprints, those numbers of residents of the informal settlements are much higher in the more actual deep learning derived dataset. A strong increase could also be found in the population exposed to medium and high landslide hazard. Further analyses facilitate the impression, that the poorer and the more vulnerable population has distinctively higher exposure to considerable landslide hazard, when using the deep-learning derived dataset. These findings show the strength of remote sensing techniques in order to retrieve actual building footprint data, that is clearly important for the estimation of potential consequences of landslide-prone areas