Deep learning & remote sensing : pushing the frontiers in image segmentation

Dissertação (Mestrado em Informática) — Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, Brasília, 2022.A segmentação de imagens visa simplificar o entendimento de imagens digitais e métodos de aprendizado profundo usando redes neurais convolucionais permitem a exploração de diferentes tarefas (e.g., segmentação semântica, instância e panóptica). A segmentação semântica atribui uma classe a cada pixel em uma imagem, a segmentação de instância classifica objetos a nível de pixel com um identificador exclusivo para cada alvo e a segmentação panóptica combina instâncias com diferentes planos de fundo. Os dados de sensoriamento remoto são muito adequados para desenvolver novos algoritmos. No entanto, algumas particularidades impedem que o sensoriamento remoto com imagens orbitais e aéreas cresça quando comparado às imagens tradicionais (e.g., fotos de celulares): (1) as imagens são muito extensas, (2) apresenta características diferentes (e.g., número de canais e formato de imagem), (3) um grande número de etapas de préprocessamento e pós-processamento (e.g., extração de quadros e classificação de cenas grandes) e (4) os softwares para rotulagem e treinamento de modelos não são compatíveis. Esta dissertação visa avançar nas três principais categorias de segmentação de imagens. Dentro do domínio de segmentação de instâncias, propusemos três experimentos. Primeiro, aprimoramos a abordagem de segmentação de instância baseada em caixa para classificar cenas grandes. Em segundo lugar, criamos um método sem caixas delimitadoras para alcançar resultados de segmentação de instâncias usando modelos de segmentação semântica em um cenário com objetos esparsos. Terceiro, aprimoramos o método anterior para cenas aglomeradas e desenvolvemos o primeiro estudo considerando aprendizado semissupervisionado usando sensoriamento remoto e dados GIS. Em seguida, no domínio da segmentação panóptica, apresentamos o primeiro conjunto de dados de segmentação panóptica de sensoriamento remoto e dispomos de uma metodologia para conversão de dados GIS no formato COCO. Como nosso primeiro estudo considerou imagens RGB, estendemos essa abordagem para dados multiespectrais. Por fim, melhoramos o método box-free inicialmente projetado para segmentação de instâncias para a tarefa de segmentação panóptica. Esta dissertação analisou vários métodos de segmentação e tipos de imagens, e as soluções desenvolvidas permitem a exploração de novas tarefas , a simplificação da rotulagem de dados e uma forma simplificada de obter previsões de instância e panópticas usando modelos simples de segmentação semântica.Image segmentation aims to simplify the understanding of digital images. Deep learning-based methods using convolutional neural networks have been game-changing, allowing the exploration of different tasks (e.g., semantic, instance, and panoptic segmentation). Semantic segmentation assigns a class to every pixel in an image, instance segmentation classifies objects at a pixel level with a unique identifier for each target, and panoptic segmentation combines instancelevel predictions with different backgrounds. Remote sensing data largely benefits from those methods, being very suitable for developing new DL algorithms and creating solutions using top-view images. However, some peculiarities prevent remote sensing using orbital and aerial imagery from growing when compared to traditional ground-level images (e.g., camera photos): (1) The images are extensive, (2) it presents different characteristics (e.g., number of channels and image format), (3) a high number of pre-processes and post-processes steps (e.g., extracting patches and classifying large scenes), and (4) most open software for labeling and deep learning applications are not friendly to remote sensing due to the aforementioned reasons. This dissertation aimed to improve all three main categories of image segmentation. Within the instance segmentation domain, we proposed three experiments. First, we enhanced the box-based instance segmentation approach for classifying large scenes, allowing practical pipelines to be implemented. Second, we created a bounding-box free method to reach instance segmentation results by using semantic segmentation models in a scenario with sparse objects. Third, we improved the previous method for crowded scenes and developed the first study considering semi-supervised learning using remote sensing and GIS data. Subsequently, in the panoptic segmentation domain, we presented the first remote sensing panoptic segmentation dataset containing fourteen classes and disposed of software and methodology for converting GIS data into the panoptic segmentation format. Since our first study considered RGB images, we extended our approach to multispectral data. Finally, we leveraged the box-free method initially designed for instance segmentation to the panoptic segmentation task. This dissertation analyzed various segmentation methods and image types, and the developed solutions enable the exploration of new tasks (such as panoptic segmentation), the simplification of labeling data (using the proposed semi-supervised learning procedure), and a simplified way to obtain instance and panoptic predictions using simple semantic segmentation models

Relative roles of genetic and epigenetic variation on the ecology and evolution of mangrove killifishes (Kryptolebias spp.)

The field of ecological epigenetics aims to understand the implications of epigenetic modifications in adaptation, inheritance and ultimately, evolution. Many questions remain open within ecological epigenetics, in particular, how epigenetic variation is influenced by genetic background, the extent of environmentally-induced epigenetic variants, as well as its degree of heritability. This thesis used the unique diversity of mating systems present in the killifish genus Kryptolebias to investigate how genetic and environmental variation shape epigenetic variation in animals. Genetic and epigenetic structure was investigated in natural populations of K. hermaphroditus in northeast Brazil, with the species being confirmed as the second example of mixed-mating system in vertebrates. Cytosine methylation was largely influenced by genetic background. However, within-populations, when individuals were more genetically similar, DNA methylation was mostly affected by parasites. Kryptolebias ocellatus, here confirmed as an outcrossing-only androdioecious species, showed deep genetic structure in southeast Brazil. Hybridisation between K. ocellatus and the predominantly selfing K. hermaphroditus was uncovered, representing the first example of hybridisation between species with different mating systems in vertebrates. Hybrids had intermediate patterns of cytosine methylation relatively to the parental species, with important biological processes being potentially misregulated. Environmental enrichment was shown to affect brain cytosine methylation patterns in two inbred strains of K. marmoratus, however genetic background had a stronger effect than environmental variation. Commonly-affected epialleles between genotypes predominantly showed a genotype-by-environment reaction norm, suggesting that exclusively environmentally-induced epialleles may be rare. Intergenerationally, parental activity affected offspring activity, and a limited number of putative intergenerational epialleles were identified. This is the first example of behavioural parental effects induced by environmental enrichment in fish. These findings show that genetic background has a prominent effect and must be take into account when evaluating the evolutionary potential of cytosine methylation variation. In addition, inheritance of environmentally-induced cytosine methylation epialleles may be limited, with other epigenetic mechanisms, such as microRNAs, being more likely to escape epigenetic reprogramming and transmit epigenetically-induced parental effects

Integrating Ecohydraulics in River Restoration: Advances in Science and Applications

Rivers have been intensively degraded due to increasing anthropogenic impacts from a growing population in a continuously developing world. Accordingly, most rivers suffer from pressures as a result of increasing dam and weir construction, habitat degradation, flow regulation, water pollution/abstraction, and the spread of invasive species. Science-based knowledge regarding solutions to counteract the effects of river degradation, and melding principles of aquatic ecology and engineering hydraulics, is thus urgently needed to guide present and future river restoration actions. This Special Issue gathers a coherent set of studies from different geographic contexts, on fundamental and applied research regarding the integration of ecohydraulics in river restoration, ranging from field studies to laboratory experiments that can be applied to real-world challenges. It contains 13 original papers covering ecohydraulic issues such as river restoration technologies, sustainable hydropower, fish passage designs and operational criteria, and habitat modeling. All papers were reviewed by international experts in ecology, hydraulics, aquatic biology, engineering, geomorphology, and hydrology. The papers herein well represent the wide applicability of ecohydraulics in river restoration and serve as a basis to improve current knowledge and management and to reduce arguments between different interests and opinions

GIS and Remote Sensing for Renewable Energy Assessment and Maps

This book aims at providing the state-of-the-art on all of the aforementioned tools in different energy applications and at different scales, i.e., urban, regional, national, and even continental for renewable scenarios planning and policy making

Renewable Energy Resource Assessment and Forecasting

In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources

Faculty Publications and Creative Works 2005

Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. Published by the Office of the Vice President for Research and Economic Development, it serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM. In 2005, UNM faculty produced over 1,887 works, including 1,887 scholarly papers and articles, 57 books, 127 book chapters, 58 reviews, 68 creative works and 4 patented works. We are proud of the accomplishments of our faculty which are in part reflected in this book, which illustrates the diversity of intellectual pursuits in support of research and education at the University of New Mexico

Abstracts of manuscripts submitted in 1993 for publication

This volume contains the abstracts of manuscripts submitted for publication during calendar year 1993 by the staff and students of the Woods Hole Oceanographic Institution. We identify the journal of those manuscripts which are in press or have been published. The volume is intended to be informative, but not a bibliography. The abstracts are listed by title in the Table of Contents and ar grouped into one of our five departents, Marine Policy Center, Coastal Research Center, or the student category. An author index is presented in the back to facilitate locating specific papers