Promoting Connectivity of Network-Like Structures by Enforcing Region Separation

We propose a novel, connectivity-oriented loss function for training deep convolutional networks to reconstruct network-like structures, like roads and irrigation canals, from aerial images. The main idea behind our loss is to express the connectivity of roads, or canals, in terms of disconnections that they create between background regions of the image. In simple terms, a gap in the predicted road causes two background regions, that lie on the opposite sides of a ground truth road, to touch in prediction. Our loss function is designed to prevent such unwanted connections between background regions, and therefore close the gaps in predicted roads. It also prevents predicting false positive roads and canals by penalizing unwarranted disconnections of background regions. In order to capture even short, dead-ending road segments, we evaluate the loss in small image crops. We show, in experiments on two standard road benchmarks and a new data set of irrigation canals, that convnets trained with our loss function recover road connectivity so well, that it suffices to skeletonize their output to produce state of the art maps. A distinct advantage of our approach is that the loss can be plugged in to any existing training setup without further modifications

Image super-resolution with dense-sampling residual channel-spatial attention networks for multi-temporal remote sensing image classification

Image super-resolution (SR) techniques can benefit a wide range of applications in the remote sensing (RS) community, including image classification. This issue is particularly relevant for image classification on time series data, considering RS datasets that feature long temporal coverage generally have a limited spatial resolution. Recent advances in deep learning brought new opportunities for enhancing the spatial resolution of historic RS data. Numerous convolutional neural network (CNN)-based methods showed superior performance in terms of developing efficient end-to-end SR models for natural images. However, such models were rarely exploited for promoting image classification based on multispectral RS data. This paper proposes a novel CNNbased framework to enhance the spatial resolution of time series multispectral RS images. Thereby, the proposed SR model employs Residual Channel Attention Networks (RCAN) as a backbone structure, whereas based on this structure the proposed models uniquely integrate tailored channel-spatial attention and dense-sampling mechanisms for performance improvement. Subsequently, state-of-the-art CNN-based classifiers are incorporated to produce classification maps based on the enhanced time series data. The experiments proved that the proposed SR model can enable unambiguously better performance compared to RCAN and other (deep learning-based) SR techniques, especially in a domain adaptation context, i.e., leveraging Sentinel-2 images for generating SR Landsat images. Furthermore, the experimental results confirmed that the enhanced multi-temporal RS images can bring substantial improvement on fine-grained multi-temporal land use classification

Detecção de estradas rurais em imagens Planet usando rede convolutional U-Net

Orientador: Prof. Dr. Jorge Antônio Silva CentenoCoorientador: Dr. Mario Ernesto Jijón PalmaDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências da Terra, Programa de Pós-Graduação em Ciências Geodésicas. Defesa : Curitiba, 25/08/2023Inclui referênciasResumo: O Brasil, um dos países mais extensos do mundo, possui uma significativa parcela de vias de rodagem situada em ambiente rural, sem a devida manutenção, o que dificulta a extensão de serviços à população rural. Muitas dessas estradas desempenham um papel fundamental na gestão territorial, uma vez que são responsáveis pelo escoamento da produção agrícola do interior do país e pela conectividade das comunidades rurais. A manutenção destas estradas e sua exploração para a extensão de serviços básicos, como energia e água, é somente possível com uma adequada atualização cartográfica da rede viária. Mais recentemente, o uso de métodos de aprendizado profundo para a análise de imagens orbitais tem crescido significativamente. Dentro desta nova realidade, esta pesquisa propõe uma abordagem baseada em técnicas de sensoriamento remoto aliadas a ferramentas de inteligência artificial, com o intuito de contribuir para solucionar o problema do mapeamento de estradas em áreas rurais. Para isto, se propõe o uso das redes convolucionais. Utilizando a arquitetura U-Net, foi possível identificar um potencial promissor na detecção de estradas rurais em imagens da constelação Planet. A taxa de detecção alcançada foi notável, atingindo uma acurácia de 92%. Contudo, é importante ressaltar a necessidade de aprimoramentos, visto que outras métricas de avaliação, como a precisão (76,66%) e o f1-score (69,48%), indicam margem para otimização dos parâmetros utilizados. No estudo também é feita uma análise comparativa entre o uso dos interpretadores na nuvem, do Google Colab (em ambiente virtual) e Pyzo (em ambiente local, utilizando o computador desktop/workstation fornecido pela UFPR). Verificou-se que o Colab apresenta vantagens em termos de custo e acesso a recursos de processamento. Entretanto, é relevante destacar que o uso do Colab também traz consigo algumas limitações, as quais requerem uma abordagem cuidadosa ao ajustar a complexidade do modelo e o tamanho do conjunto de dados.Abstract: Brazil, one of the largest countries in the world, has a significant number of roads in rural areas that are not properly maintained, making it difficult to extend services to the rural population. Many of these roads play a fundamental role in land management, as they are responsible for transporting agricultural produce from the interior of the country and for connecting rural communities. Maintaining these roads and exploiting them to extend basic services, such as energy and water, is only possible with a proper cartographic update of the road network. More recently, the use of deep learning methods to analyse orbital images has grown significantly. Within this new reality, this research proposes an approach based on remote sensing techniques combined with artificial intelligence tools, with the aim of helping to solve the problem of mapping roads in rural areas. To this end, the use of convolutional networks is proposed. Using the U-Net architecture, it was possible to identify promising potential for detecting rural roads in images from the Planet constellation. The detection rate achieved was remarkable, reaching an accuracy of 92 per cent. However, it is important to highlight the need for improvement, since other evaluation metrics, such as accuracy (76.66%) and f1-score (69.48%), indicate room for optimization of the parameters used. The study also makes a comparative analysis between the use of interpreters in the cloud, Google Colab (in a virtual environment) and Pyzo (in a local environment, using the desktop/workstation computer provided by UFPR). Colab was found to have advantages in terms of cost and access to processing resources. However, it is important to emphasize that the use of Colab also brings with it some limitations, which require a careful approach when adjusting the complexity of the model and the size of the data set

Satellite Observations and Spatiotemporal Assessment of Salt Marsh /Dieback Along Coastal South Carolina (1990-2019)

Coastal wetland mapping is often difficult because of the heterogeneous vegetation compositions and associated tidal effects. Past studies in the Gulf/Atlantic coast states have reported acute marsh dieback events in which marsh rapidly browned and thinned, leaving stubble of dead stems or mudflad with damaged ecosystem services. Reported marsh dieback in South Carolina (SC), USA, however, have been limited. Previous studies have suggested a suite of possibly abiotic and biotic attributes responsible for salt marsh dieback. However, there are no consensus answers in current literature explaining what led to marsh dieback in past decades, especially from the spatiotemporal perspective. In this study, the U-Net was employed, and an adaptive deep learning approach was developed to map statewide salt marshes in estuarine emergent wetlands of SC from 20 Sentinel-2A&B images. Then all marsh dieback events were identified in the North Inlet-Winyah Bay (NIWB) estuary, SC, from 1990 to 2019. With 30 annually collected Landsat images, the Normalized Difference Vegetation Index (NDVI) series was extracted. A Stacked Denoising Autoencoder neural network was developed to identify the NDVI anomalies on the trajectories. All marsh dieback patches were extracted, and their inter-annual changes were examined. Among these were the five most severe marsh dieback events (1991, 1999, 2000, 2002, and 2013). The spatiotemporal relationships between the dieback series and the associated environmental variables in an intertidal marsh in the estuary were investigated. Daily Evaporative Demand Drought Index (EDDI), daily precipitation data from Parameter Elevation Regressions on Independent Slopes Model (PRISM), and station-based water quality observations (dissolved oxygen, specific conductivity, salinity, turbidity, pH, and temperature) in the estuary were retrieved. This study cogitates the environmental influence on coastal marsh from a spatiotemporal perspective using a long-term satellite time series analysis. The findings could provide insights into marsh ecological resilience and facilitate coastal ecosystem management