3,030 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure
Towards an integrated vulnerability-based approach for evaluating, managing and mitigating earthquake risk in urban areas
Tese de doutoramento em Civil EngineeringSismos de grande intensidade, como aqueles que ocorreram na Turquía-Síria (2023) ou México (2017)
deviam chamar a atenção para o projeto e implementação de ações proativas que conduzam à identificação
de bens vulneráveis. A presente tese propõe um fluxo de trabalho relativamente simples para
efetuar avaliações da vulnerabilidade sísmica à escala urbana mediante ferramentas digitais. Um modelo
de vulnerabilidade baseado em parâmetros é adotado devido à afinidade que possui com o Catálogo Nacional
de Monumentos Históricos mexicano. Uma primeira implementação do método (a grande escala)
foi efetuada na cidade histórica de Atlixco (Puebla, México), demonstrando a sua aplicabilidade e algumas
limitações, o que permitiu o desenvolvimento de uma estratégia para quantificar e considerar as incertezas
epistémicas encontradas nos processos de aquisição de dados. Devido ao volume de dados tratado, foi
preciso desenvolver meios robustos para obter, armazenar e gerir informações. O uso de Sistemas de
Informação Geográfica, com programas à medida baseados em linguagem Python e a distribuição de
ficheiros na ”nuvem”, facilitou a criação de bases de dados de escala urbana para facilitar a aquisição de
dados em campo, os cálculos de vulnerabilidade e dano e, finalmente, a representação dos resultados.
Este desenvolvimento foi a base para um segundo conjunto de trabalhos em municípios do estado de
Morelos (México). A caracterização da vulnerabilidade sísmica de mais de 160 construções permitiu a
avaliação da representatividade do método paramétrico pela comparação entre os níveis de dano teórico
e os danos observados depois do terramoto de Puebla-Morelos (2017). Esta comparação foi a base para
efetuar processos de calibração e ajuste assistidos por algoritmos de aprendizagem de máquina (Machine
Learning), fornecendo bases para o desenvolvimento de modelos de vulnerabilidade à medida (mediante
o uso de Inteligência Artificial), apoiados nas evidências de eventos sísmicos prévios.Strong seismic events like the ones of Türkiye-Syria (2023) or Mexico (2017) should guide our attention
to the design and implementation of proactive actions aimed to identify vulnerable assets. This work is
aimed to propose a suitable and easy-to-implement workflow for performing large-scale seismic vulnerability
assessments in historic environments by means of digital tools. A vulnerability-oriented model based
on parameters is adopted given its affinity with the Mexican Catalogue of Historical Monuments. A first
large-scale implementation of this method in the historical city of Atlixco (Puebla, Mexico) demonstrated its
suitability and some limitations, which lead to develop a strategy for quantifying and involving the epistemic
uncertainties found during the data acquisition process. Given the volume of data that these analyses involve,
it was necessary to develop robust data acquisition, storing and management strategies. The use
of Geographical Information System environments together with customised Python-based programs and
cloud-based distribution permitted to assemble urban databases for facilitating field data acquisition, performing
vulnerability and damage calculations, and representing outcomes. This development was the
base for performing a second large-scale assessment in selected municipalities of the state of Morelos
(Mexico). The characterisation of the seismic vulnerability of more than 160 buildings permitted to assess
the representativeness of the parametric vulnerability approach by comparing the theoretical damage estimations against the damages observed after the Puebla-Morelos 2017 Earthquakes. Such comparison is
the base for performing a Machine Learning assisted process of calibration and adjustment, representing
a feasible strategy for calibrating these vulnerability models by using Machine-Learning algorithms and the
empirical evidence of damage in post-seismic scenarios.This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit
Institute for Sustainability and Innovation in Structural Engineering (ISISE), reference UIDB/04029/2020.
This research had financial support provided by the Portuguese Foundation of Science and Technology
(FCT) through the Analysis and Mitigation of Risks in Infrastructures (InfraRisk) program under the PhD
grant PD/BD/150385/2019
Frontiers of Humanity and Beyond: Towards new critical understandings of borders. Working Papers
UIDB/04666/2020
UIDP/04666/2020publishersversionpublishe
Vulnerability of the Nigerian coast and communities to climate change induced coastal erosion
Improving coastal resilience to climate change hazards requires understanding past shoreline changes. As the coastal population grows, evaluation and monitoring of shoreline changes are essential for planning and development. Population growth increases exposure to sea level rise and coastal hazards. Nigeria, where the study is situated, is among the top fifteen countries in the world for coastal population exposure to sea level rise. This study provided a novel lens in establishing a link between social factors and the intensifying coastal erosion along the Akwa Ibom State study coast. The mixed-method approach used in the study to assess the vulnerability of the Nigerian coast and communities to climate change-induced coastal erosion proved to be essential in gathering a wide range of data (physical, socio economic, participatory GIS maps and social learning) that contributed to a more robust and holistic assessment of coastal erosion, which is a complex issue due to the interplay between the human and natural environments. Remotely sensed data was used to examine the susceptibility and coastal evolution of Akwa Ibom State over 36 years (1984 -2020). Longer-term (1984- 2020) and short-term (2015-2020) shoreline change analyses were used to understand coastal erosion and accretion. From 1984-2020, the total average linear regression rate (LRR) was - 2.7+0.18m/yr and from 2015-2020, it was -3.94 +1.28m/yr, demonstrating an erosional trend along the study coast. Although the rate of erosion varies along the study coast, the linear regression rates (LRR) results show a predominant trend of erosion in both the short and longer term. According to the 2022 Intergovernmental Panel on Climate Change report, loss of land, loss of assets, community disruption and livelihood, loss of environmental resources, ecosystem, loss of life, or adverse health impact are all potential risks along the African coast due to climate change – this study shows that these risks are already occurring today. To quantify the anticipated future coastal erosion risk by 2040 along the study coast, the findings in this study show an overall average LRR of -2.73+ 0.99 m/yr which anticipates that coastal erosion will still be prevalent along the coast by 2040. And, given the current global climate change situation, should be expected to be much higher than the current forecasting.
This study re-conceptualised the European Environmental Agency Driver-Pressure StateImpact-Response (DPSIR) model to show Hazard-Driver-Pressure-State-Impact ResponseObservation causal linkages to coastal erosion hazards. The results showed how human activities and environmental interactions have evolved through time, causing coastal erosion. Removal of vegetation cover/backstop for residential and agricultural purposes, indicate that human activities significantly contribute to the study area's susceptibility, rapid shoreline changes, and vulnerability to coastal erosion, in addition to oceanic and climate change drivers such as sea level rise and storminess. Risk perception of coastal erosion in the study area was analysed using the rhizoanalytic method proposed by Deleueze. The method demonstrates how connections and movements can be related and how data can be used to show multiplicity, mark and unmark ideas, rupture pre-conceptions and make new connections.
This study shows that coastal erosion awareness is insufficient to build a long-term management plan and sustain coastal resilience. The Hino's conceptual model which provides in-depth understanding on planned retreat was used to illustrate migratory and planned retreat for the study coast where relocation has already occurred due to coastal erosion. The result fell within the Self-Reliance quadrant, indicating that people left the risk zone without government backing or retreat plans. Other coastal residents who have not relocated fell within the Hunkered Down quadrant, showing that they are willing to stay in the risk zone and cope with the threat unless the government/environmental agencies relocate them. This study shows that coastal resilience requires adaptive capacity and government support. However, multilevel governance has inhibited government-community dialogue and involvement, increasing coastal erosion vulnerability. The coastal vulnerability index to coastal erosion was calculated using the Analytical Hierarchy Process weightings. It revealed that 67.55% of the study coast falls within the high-very high vulnerability class while 32.45% is within the very low-low vulnerability class. This study developed and combined a risk perception index to coastal erosion (RPIerosion) and participatory GIS (PGIS) mapping into a novel coastal vulnerability index called the integrated coastal erosion vulnerability index (ICEVI). The case study evaluation in Akata, showed an improvement in the overall vulnerability assessment to reflect the real-world scenario, which was consistent with field data.
This study demonstrated not only the presence and challenges of coastal erosion in the research area but also the relevance of involvement between the local stakeholders, government and environmental agencies. Thus, showing the potential for the perspectives of the inhabitants of these regions to inform the understanding of the resilience capacity of the people impacted, and importantly to inform future co-design and/or selection of effective adaptation methods, to better support coastal climate change resilience in these communities. Overall, the study provides a useful contribution to coastal erosion vulnerability assessments in data-scarce regions more broadly, where the mixed-methods approach used here can be applied elsewhere
Weather or not? The role of international sanctions and climate on food prices in Iran
IntroductionThe scarcity of resources have affected food production, which has challenged the ability of Iran to provide adequate food for the population. Iterative and mounting sanctions on Iran by the international community have seriously eroded Iran's access to agricultural technology and resources to support a growing population. Limited moisture availability also affects Iran's agricultural production. The aim of this study was to analyze the influence of inflation, international sanctions, weather disturbances, and domestic crop production on the price of rice, wheat and lentils from 2010 to 2021 in Iran.MethodData were obtained from the statistical yearbooks of the Ministry of Agriculture in Iran, Statistical Center of Iran, and the Central Bank of Iran. We analyzed econometric measures of food prices, including CPI, food inflation, subsidy reform plan and sanctions to estimate economic relationships. After deflating the food prices through CPI and detrending the time series to resolve the non-linear issue, we used monthly Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation data to analyze the influence of weather disturbances on food prices.Results and discussionThe price of goods not only provides an important indicator of the balance between agricultural production and market demand, but also has strong impacts on food affordability and food security. This novel study used a combination of economic and climate factors to analyze the food prices in Iran. Our statistical modeling framework found that the monthly precipitation on domestic food prices, and ultimately food access, in the country is much less important than the international sanctions, lowering Iran's productive capability and negatively impacting its food security
Multimodal spatio-temporal deep learning framework for 3D object detection in instrumented vehicles
This thesis presents the utilization of multiple modalities, such as image and lidar, to incorporate spatio-temporal information from sequence data into deep learning architectures for 3Dobject detection in instrumented vehicles. The race to autonomy in instrumented vehicles or self-driving cars has stimulated significant research in developing autonomous driver assistance systems (ADAS) technologies related explicitly to perception systems. Object detection plays a crucial role in perception systems by providing spatial information to its subsequent modules; hence, accurate detection is a significant task supporting autonomous driving. The advent of deep learning in computer vision applications and the availability of multiple sensing modalities such as 360° imaging, lidar, and radar have led to state-of-the-art 2D and 3Dobject detection architectures. Most current state-of-the-art 3D object detection frameworks consider single-frame reference. However, these methods do not utilize temporal information associated with the objects or scenes from the sequence data. Thus, the present research hypothesizes that multimodal temporal information can contribute to bridging the gap between 2D and 3D metric space by improving the accuracy of deep learning frameworks for 3D object estimations. The thesis presents understanding multimodal data representations and selecting hyper-parameters using public datasets such as KITTI and nuScenes with Frustum-ConvNet as a baseline architecture. Secondly, an attention mechanism was employed along with convolutional-LSTM to extract spatial-temporal information from sequence data to improve 3D estimations and to aid the architecture in focusing on salient lidar point cloud features. Finally, various fusion strategies are applied to fuse the modalities and temporal information into the architecture to assess its efficacy on performance and computational complexity. Overall, this thesis has established the importance and utility of multimodal systems for refined 3D object detection and proposed a complex pipeline incorporating spatial, temporal and attention mechanisms to improve specific, and general class accuracy demonstrated on key autonomous driving data sets
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