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

Bounding Box-Free Instance Segmentation Using Semi-Supervised Learning for Generating a City-Scale Vehicle Dataset

Vehicle classification is a hot computer vision topic, with studies ranging from ground-view up to top-view imagery. In remote sensing, the usage of top-view images allows for understanding city patterns, vehicle concentration, traffic management, and others. However, there are some difficulties when aiming for pixel-wise classification: (a) most vehicle classification studies use object detection methods, and most publicly available datasets are designed for this task, (b) creating instance segmentation datasets is laborious, and (c) traditional instance segmentation methods underperform on this task since the objects are small. Thus, the present research objectives are: (1) propose a novel semi-supervised iterative learning approach using GIS software, (2) propose a box-free instance segmentation approach, and (3) provide a city-scale vehicle dataset. The iterative learning procedure considered: (1) label a small number of vehicles, (2) train on those samples, (3) use the model to classify the entire image, (4) convert the image prediction into a polygon shapefile, (5) correct some areas with errors and include them in the training data, and (6) repeat until results are satisfactory. To separate instances, we considered vehicle interior and vehicle borders, and the DL model was the U-net with the Efficient-net-B7 backbone. When removing the borders, the vehicle interior becomes isolated, allowing for unique object identification. To recover the deleted 1-pixel borders, we proposed a simple method to expand each prediction. The results show better pixel-wise metrics when compared to the Mask-RCNN (82% against 67% in IoU). On per-object analysis, the overall accuracy, precision, and recall were greater than 90%. This pipeline applies to any remote sensing target, being very efficient for segmentation and generating datasets.Comment: 38 pages, 10 figures, submitted to journa

Panoptic Segmentation Meets Remote Sensing

Panoptic segmentation combines instance and semantic predictions, allowing the detection of "things" and "stuff" simultaneously. Effectively approaching panoptic segmentation in remotely sensed data can be auspicious in many challenging problems since it allows continuous mapping and specific target counting. Several difficulties have prevented the growth of this task in remote sensing: (a) most algorithms are designed for traditional images, (b) image labelling must encompass "things" and "stuff" classes, and (c) the annotation format is complex. Thus, aiming to solve and increase the operability of panoptic segmentation in remote sensing, this study has five objectives: (1) create a novel data preparation pipeline for panoptic segmentation, (2) propose an annotation conversion software to generate panoptic annotations; (3) propose a novel dataset on urban areas, (4) modify the Detectron2 for the task, and (5) evaluate difficulties of this task in the urban setting. We used an aerial image with a 0,24-meter spatial resolution considering 14 classes. Our pipeline considers three image inputs, and the proposed software uses point shapefiles for creating samples in the COCO format. Our study generated 3,400 samples with 512x512 pixel dimensions. We used the Panoptic-FPN with two backbones (ResNet-50 and ResNet-101), and the model evaluation considered semantic instance and panoptic metrics. We obtained 93.9, 47.7, and 64.9 for the mean IoU, box AP, and PQ. Our study presents the first effective pipeline for panoptic segmentation and an extensive database for other researchers to use and deal with other data or related problems requiring a thorough scene understanding.Comment: 40 pages, 10 figures, submitted to journa

ANÁLISE DAS DESAPROPRIAÇÕES DE TERRAS NOS ESTUDOS DE VIABILIDADE DE OBRAS DE INFRAESTRUTURA DE TRANSPORTES

The land expropriation process is a huge problem for transport infrastructure projects, since this process is always slow and socially sensitive. Therefore, adoption of strategies to mitigate the expropriation problems is essential to avoid future problems. The use of simulation program is an important tool for planning, allowing analyze different scenarios and improve path option from different variables in a decision matrix. Thus, previous studies of expropriation provide elements that enable to define measures to mitigate the social impacts and risks during the works. The objective of this research is to develop a methodology for preliminary analysis of expropriation in federal railways studies. In the current study is considered one of the simulated trajectories for the high-speed train between Rio de Janeiro and São Paulo. The result shows that the hypothetical railway produces low social impact in comparison with other federal transportation routes.O processo de desapropriação de terras é um grande problema para projetos de infraestrutura de transporte, uma vez que este processo é sempre lento e socialmente sensível. Portanto, a adoção de estratégias para mitigar os problemas de desapropriação é essencial para evitar problemas futuros. O emprego de simulações é uma ferramenta importante para o planejamento, permitindo analisar diferentes cenários e definir a melhor opção do trajeto a partir de diferentes variáveis em uma matriz de decisão. Portanto, os estudos anteriores de desapropriação fornecem elementos que permitem definir medidas para mitigar os impactos sociais e riscos durante as obras. O objetivo dessa pesquisa é desenvolver uma metodologia para a análise preliminar de desapropriação em estudos de vias federais. No presente estudo é analisada uma das trajetórias simuladas para uma ferrovia para trens de alta velocidade entre o Rio de Janeiro e São Paulo. O resultado demonstra que a hipotética via férrea produz baixos impactos sociais, em comparação com outras vias federais

Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation

Brazil is a tropical country with continental dimensions and abundant solar resources that are still underutilized. However, solar energy is one of the most promising renewable sources in the country. The proper inspection of Photovoltaic (PV) solar plants is an issue of great interest for the Brazilian territory’s energy management agency, and advances in computer vision and deep learning allow automatic, periodic, and low-cost monitoring. The present research aims to identify PV solar plants in Brazil using semantic segmentation and a mosaicking approach for large image classification. We compared four architectures (U-net, DeepLabv3+, Pyramid Scene Parsing Network, and Feature Pyramid Network) with four backbones (Efficient-net-b0, Efficient-net-b7, ResNet-50, and ResNet-101). For mosaicking, we evaluated a sliding window with overlapping pixels using different stride values (8, 16, 32, 64, 128, and 256). We found that: (1) the models presented similar results, showing that the most relevant approach is to acquire high-quality labels rather than models in many scenarios; (2) U-net presented slightly better metrics, and the best configuration was U-net with the Efficient-net-b7 encoder (98% overall accuracy, 91% IoU, and 95% F-score); (3) mosaicking progressively increases results (precision-recall and receiver operating characteristic area under the curve) when decreasing the stride value, at the cost of a higher computational cost. The high trends of solar energy growth in Brazil require rapid mapping, and the proposed study provides a promising approach

Rice crop detection using LSTM, Bi-LSTM, and machine learning models from sentinel-1 time series

The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel-1 program expands studies of radar data (C-band) for rice monitoring at regional scales, due to the high temporal resolution and free data distribution. Recurrent Neural Network (RNN) model has reached state-of-the-art in the pattern recognition of time-sequenced data, obtaining a significant advantage at crop classification on the remote sensing images. One of the most used approaches in the RNN model is the Long Short-Term Memory (LSTM) model and its improvements, such as Bidirectional LSTM (Bi-LSTM). Bi-LSTM models are more effective as their output depends on the previous and the next segment, in contrast to the unidirectional LSTM models. The present research aims to map rice crops from Sentinel-1 time series (band C) using LSTM and Bi-LSTM models in West Rio Grande do Sul (Brazil). We compared the results with traditional Machine Learning techniques: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Normal Bayes (NB). The developed methodology can be subdivided into the following steps: (a) acquisition of the Sentinel time series over two years; (b) data pre-processing and minimizing noise from 3D spatial-temporal filters and smoothing with Savitzky-Golay filter; (c) time series classification procedures; (d) accuracy analysis and comparison among the methods. The results show high overall accuracy and Kappa (>97% for all methods and metrics). Bi-LSTM was the best model, presenting statistical differences in the McNemar test with a significance of 0.05. However, LSTM and Traditional Machine Learning models also achieved high accuracy values. The study establishes an adequate methodology for mapping the rice crops in West Rio Grande do Sul

Assessment of gully development using geomorphic change detection between pre-and post-urbanization scenarios

Urbanization processes have caused changes in the runoff behavior, especially by impervious surfaces produced by paving and buildings. Impermeable surfaces prevent the infiltration of rainwater, increasing the volume and speed of runoff. Besides, inadequate urban planning coupled with heavy rains promotes the evolution of erosion processes, especially in peri-urban areas. This research aims to identify spatial patterns of geomorphic change in the gully areas due to urbanization in the city of Jacareí (SP). The methodology has the following steps: (1) elaboration of the Digital Elevation Model (DEM) from stereophotogrammetric techniques; (2) elaboration of the pre- and post-urbanization DEM; (3) extraction of contributing area using the D-Infinity method and of the topographic indices (topographic wetness, stream power, and compound topographic); and (4) calculate the difference between the pre- and post-urbanization topographic attributes. The preparation of the pre- and post-urbanization DEM used the MATCH-T DSM and DTMaster modules, both belonging to the INPHO system. Photogrammetric techniques allow the generation of digital models suitable for hydrological studies. The urbanization exposed an evident influence on the triggering of erosion, evidencing an increase of all topographic indices in areas that develop gullies

Rethinking panoptic segmentation in remote sensing : a hybrid approach using semantic segmentation and non-learning methods

This letter proposes a novel method to obtain panoptic predictions by extending the semantic segmentation task with a few non-learning image processing steps, presenting the following benefits: 1) annotations do not require a specific format [e.g., common objects in context (COCO)]; 2) fewer parameters (e.g., single loss function and no need for object detection parameters); and 3) a more straightforward sliding windows implementation for large image classification (still unexplored for panoptic segmentation). Semantic segmentation models do not individualize touching objects, as their predictions can merge; i.e., a single polygon represents many targets. Our method overcomes this problem by isolating the objects using borders on the polygons that may merge. The data preparation requires generating a one-pixel border, and for unique object identification, we create a list with the isolated polygons, attribute a different value to each one, and use the expanding border (EB) algorithm for those with borders. Although any semantic segmentation model applies, we used the U-Net with three backbones (EfficientNet-B5, EfficientNet-B3, and EfficientNet-B0). The results show that the following hold: 1) the EfficientNet-B5 had the best results with 70% mean intersection over union (mIoU); 2) the EB algorithm presented better results for better models; 3) the panoptic metrics show a high capability of identifying things and stuff with 65 panoptic quality (PQ); and 4) the sliding windows on a 2560×2560 -pixel area has shown promising results, in which the ratio of merged objects by correct predictions was lower than 1% for all classes.Instituto de Ciências Exatas (IE)Departamento de Ciência da Computação (IE CIC)Instituto de Ciências Humanas (ICH)Departamento de Geografia (ICH GEA