101 research outputs found
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
Semantic location extraction from crowdsourced data
Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction
Geo-Information Technology and Its Applications
Geo-information technology has been playing an ever more important role in environmental monitoring, land resource quantification and mapping, geo-disaster damage and risk assessment, urban planning and smart city development. This book focuses on the fundamental and applied research in these domains, aiming to promote exchanges and communications, share the research outcomes of scientists worldwide and to put these achievements better social use. This Special Issue collects fourteen high-quality research papers and is expected to provide a useful reference and technical support for graduate students, scientists, civil engineers and experts of governments to valorize scientific research
Application of Artificial Intelligence algorithms to support decision-making in agriculture activities
Deep Learning has been successfully applied to image recognition, speech recognition, and
natural language processing in recent years. Therefore, there has been an incentive to apply
it in other fields as well. The field of agriculture is one of the most important in which the
application of artificial intelligence algorithms, and particularly, of deep learning needs to
be explored, as it has a direct impact on human well-being. In particular, there is a need
to explore how deep learning models for decision-making can be used as a tool for optimal
planting, land use, yield improvement, production/disease/pest control, and other activities.
The vast amount of data received from sensors in smart farms makes it possible to use deep
learning as a model for decision-making in this field. In agriculture, no two environments are
exactly alike, which makes testing, validating, and successfully implementing such technologies
much more complex than in most other sectors. Recent scientific developments in the
field of deep learning, applied to agriculture, are reviewed and some challenges and potential
solutions using deep learning algorithms in agriculture are discussed. Higher performance
in terms of accuracy and lower inference time can be achieved, and the models can be made
useful in real-world applications. Finally, some opportunities for future research in this area
are suggested. The ability of artificial neural networks, specifically Long Short-Term Memory
(LSTM) and Bidirectional LSTM (BLSTM), to model daily reference evapotranspiration
and soil water content is investigated. The application of these techniques to predict these
parameters was tested for three sites in Portugal. A single-layer BLSTM with 512 nodes was
selected. Bayesian optimization was used to determine the hyperparameters, such as learning
rate, decay, batch size, and dropout size. The model achieved mean square error (MSE)
values ranging from 0.07 to 0.27 (mm d–1)² for ETo (Reference Evapotranspiration) and
0.014 to 0.056 (m³m–3)² for SWC (Soil Water Content), with R2 values ranging from 0.96
to 0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate
potential performance improvement. Performance dropped in all datasets due to the
complexity of the model. The performance of the models was also compared with CNN, traditional
machine learning algorithms Support Vector Regression, and Random Forest. LSTM
achieved the best performance. Finally, the impact of the loss function on the performance
of the proposed models was investigated. The model with the mean square error (MSE) as
loss function performed better than the model with other loss functions. Afterwards, the
capabilities of these models and their extension, BLSTM and Bidirectional Gated Recurrent
Units (BGRU) to predict end-of-season yields are investigated. The models use historical
data, including climate data, irrigation scheduling, and soil water content, to estimate endof-
season yield. The application of this technique was tested for tomato and potato yields at a
site in Portugal. The BLSTM network outperformed the GRU, the LSTM, and the BGRU networks
on the validation dataset. The model was able to capture the nonlinear relationship
between irrigation amount, climate data, and soil water content and predict yield with an
MSE of 0.017 to 0.039 kg/ha. The performance of the BLSTM in the test was compared with
the most commonly used deep learning method called CNN, and machine learning methods
including a Multi-Layer Perceptrons model and Random Forest regression. The BLSTM out-performed the other models with a R2-score between 0.97 and 0.99. The results show that
analyzing agricultural data with the LSTM model improves the performance of the model in
terms of accuracy. The CNN model achieved the second-best performance. Therefore, the
deep learning model has a remarkable ability to predict the yield at the end of the season. Additionally,
a Deep Q-Network was trained for irrigation scheduling. The agent was trained to
schedule irrigation for a tomato field in Portugal. Two LSTM models trained previously were
used as the agent environment. One predicts the total water in the soil profile on the next
day. The other one was employed to estimate the yield based on the environmental condition
during a season and then measure the net return. The agent uses this information to decide
the following irrigation amount. LSTM and CNN networks were used to estimate the Q-table
during training. Unlike the LSTM model, the ANN and the CNN could not estimate the Qtable,
and the agent’s reward decreased during training. The comparison of the performance
of the model was done with fixed-base irrigation and threshold-based irrigation. The trained
model increased productivity by 11% and decreased water consumption by 20% to 30% compared
to the fixed method. Also, an on-policy model, Advantage Actor–Critic (A2C), was
implemented to compare irrigation scheduling with Deep Q-Network for the same tomato
crop. The results show that the on-policy model A2C reduced water consumption by 20%
compared to Deep Q-Network with a slight change in the net reward. These models can be
developed to be applied to other cultures with high importance in Portugal, such as fruit,
cereals, and grapevines, which also have large water requirements. The models developed
along this thesis can be re-evaluated and trained with historical data from other cultures with
high production in Portugal, such as fruits, cereals, and grapes, which also have high water
demand, to create a decision support and recommendation system that tells farmers when
and how much to irrigate. This system helps farmers avoid wasting water without reducing
productivity. This thesis aims to contribute to the future steps in the development of precision
agriculture and agricultural robotics. The models developed in this thesis are relevant to
support decision-making in agricultural activities, aimed at optimizing resources, reducing
time and costs, and maximizing production.Nos últimos anos, a técnica de aprendizagem profunda (Deep Learning) foi aplicada com
sucesso ao reconhecimento de imagem, reconhecimento de fala e processamento de linguagem
natural. Assim, tem havido um incen tivo para aplicá-la também em outros sectores.
O sector agrícola é um dos mais importantes, em que a aplicação de algoritmos de inteligência
artificial e, em particular, de deep learning, precisa ser explorada, pois tem impacto direto
no bem-estar humano. Em particular, há uma necessidade de explorar como os modelos de
aprendizagem profunda para a tomada de decisão podem ser usados como uma ferramenta
para cultivo ou plantação ideal, uso da terra, melhoria da produtividade, controlo de produção,
de doenças, de pragas e outras atividades. A grande quantidade de dados recebidos
de sensores em explorações agrícolas inteligentes (smart farms) possibilita o uso de deep
learning como modelo para tomada de decisão nesse campo. Na agricultura, não há dois
ambientes iguais, o que torna o teste, a validação e a implementação bem-sucedida dessas
tecnologias muito mais complexas do que na maioria dos outros setores. Desenvolvimentos
científicos recentes no campo da aprendizagem profunda aplicada à agricultura, são revistos
e alguns desafios e potenciais soluções usando algoritmos de aprendizagem profunda na agricultura
são discutidos. Maior desempenho em termos de precisão e menor tempo de inferência
pode ser alcançado, e os modelos podem ser úteis em aplicações do mundo real. Por fim,
são sugeridas algumas oportunidades para futuras pesquisas nesta área. A capacidade de redes
neuronais artificiais, especificamente Long Short-Term Memory (LSTM) e LSTM Bidirecional
(BLSTM), para modelar a evapotranspiração de referência diária e o conteúdo de água
do solo é investigada. A aplicação destas técnicas para prever estes parâmetros foi testada em
três locais em Portugal. Um BLSTM de camada única com 512 nós foi selecionado. A otimização
bayesiana foi usada para determinar os hiperparâmetros, como taxa de aprendizagem,
decaimento, tamanho do lote e tamanho do ”dropout”. O modelo alcançou os valores de erro
quadrático médio na faixa de 0,014 a 0,056 e R2 variando de 0,96 a 0,98. Um modelo de
Rede Neural Convolucional (CNN – Convolutional Neural Network) foi adicionado ao LSTM
para investigar uma potencial melhoria de desempenho. O desempenho decresceu em todos
os conjuntos de dados devido à complexidade do modelo. O desempenho dos modelos
também foi comparado com CNN, algoritmos tradicionais de aprendizagem máquina Support
Vector Regression e Random Forest. O LSTM obteve o melhor desempenho. Por fim,
investigou-se o impacto da função de perda no desempenho dos modelos propostos. O modelo
com o erro quadrático médio (MSE) como função de perda teve um desempenho melhor
do que o modelo com outras funções de perda. Em seguida, são investigadas as capacidades
desses modelos e sua extensão, BLSTM e Bidirectional Gated Recurrent Units (BGRU) para
prever os rendimentos da produção no final da campanha agrícola. Os modelos usam dados
históricos, incluindo dados climáticos, calendário de rega e teor de água do solo, para estimar
a produtividade no final da campanha. A aplicação desta técnica foi testada para os rendimentos
de tomate e batata em um local em Portugal. A rede BLSTM superou as redes GRU,
LSTM e BGRU no conjunto de dados de validação. O modelo foi capaz de captar a relação não
linear entre dotação de rega, dados climáticos e teor de água do solo e prever a produtividade com um MSE variando de 0,07 a 0,27 (mm d–1)² para ETo (Evapotranspiração de Referência)
e de 0,014 a 0,056 (m³m–3)² para SWC (Conteúdo de Água do Solo), com valores de R2
variando de 0,96 a 0,98. O desempenho do BLSTM no teste foi comparado com o método de
aprendizagem profunda CNN, e métodos de aprendizagem máquina, incluindo um modelo
Multi-Layer Perceptrons e regressão Random Forest. O BLSTM superou os outros modelos
com um R2 entre 97% e 99%. Os resultados mostram que a análise de dados agrícolas
com o modelo LSTM melhora o desempenho do modelo em termos de precisão. O modelo
CNN obteve o segundo melhor desempenho. Portanto, o modelo de aprendizagem profunda
tem uma capacidade notável de prever a produtividade no final da campanha. Além disso,
uma Deep Q-Network foi treinada para programação de irrigação para a cultura do tomate.
O agente foi treinado para programar a irrigação de uma plantação de tomate em Portugal.
Dois modelos LSTM treinados anteriormente foram usados como ambiente de agente. Um
prevê a água total no perfil do solo no dia seguinte. O outro foi empregue para estimar a produtividade
com base nas condições ambientais durante uma o ciclo biológico e então medir
o retorno líquido. O agente usa essas informações para decidir a quantidade de irrigação.
As redes LSTM e CNN foram usadas para estimar a Q-table durante o treino. Ao contrário
do modelo LSTM, a RNA e a CNN não conseguiram estimar a tabela Q, e a recompensa do
agente diminuiu durante o treino. A comparação de desempenho do modelo foi realizada
entre a irrigação com base fixa e a irrigação com base em um limiar. A aplicação das doses
de rega preconizadas pelo modelo aumentou a produtividade em 11% e diminuiu o consumo
de água em 20% a 30% em relação ao método fixo. Além disso, um modelo dentro da táctica,
Advantage Actor–Critic (A2C), é foi implementado para comparar a programação de
irrigação com o Deep Q-Network para a mesma cultura de tomate. Os resultados mostram
que o modelo de táctica A2C reduziu o consumo de água consumo em 20% comparado ao
Deep Q-Network com uma pequena mudança na recompensa líquida. Estes modelos podem
ser desenvolvidos para serem aplicados a outras culturas com elevada produção em Portugal,
como a fruta, cereais e vinha, que também têm grandes necessidades hídricas. Os modelos
desenvolvidos ao longo desta tese podem ser reavaliados e treinados com dados históricos
de outras culturas com elevada importância em Portugal, tais como frutas, cereais e uvas,
que também têm elevados consumos de água. Assim, poderão ser desenvolvidos sistemas
de apoio à decisão e de recomendação aos agricultores de quando e quanto irrigar. Estes
sistemas poderão ajudar os agricultores a evitar o desperdício de água sem reduzir a produtividade.
Esta tese visa contribuir para os passos futuros na evolução da agricultura de
precisão e da robótica agrícola. Os modelos desenvolvidos ao longo desta tese são relevantes
para apoiar a tomada de decisões em atividades agrícolas, direcionadas à otimização de recursos,
redução de tempo e custos, e maximização da produção.Centro-01-0145-FEDER000017-EMaDeS-Energy,
Materials, and Sustainable Development, co-funded by the Portugal 2020 Program (PT 2020),
within the Regional Operational Program of the Center (CENTRO 2020) and the EU through
the European Regional Development Fund (ERDF). Fundação para a Ciência e a Tecnologia
(FCT—MCTES) also provided financial support via project UIDB/00151/2020 (C-MAST).
It was also supported by the R&D Project BioDAgro – Sistema operacional inteligente de
informação e suporte á decisão em AgroBiodiversidade, project PD20-00011, promoted by
Fundação La Caixa and Fundação para a Ciência e a Tecnologia, taking place at the C-MAST
- Centre for Mechanical and Aerospace Sciences and Technology, Department of Electromechanical
Engineering of the University of Beira Interior, Covilhã, Portugal
Visual Place Recognition under Severe Viewpoint and Appearance Changes
Over the last decade, the eagerness of the robotic and computer vision research communities unfolded extensive advancements in long-term robotic vision. Visual localization is the constituent of this active research domain; an ability of an object to correctly localize itself while mapping the environment simultaneously, technically termed as Simultaneous Localization and Mapping (SLAM).
Visual Place Recognition (VPR), a core component of SLAM is a well-known paradigm. In layman terms, at a certain place/location within an environment, a robot needs to decide whether it’s the same place experienced before? Visual Place Recognition utilizing Convolutional Neural Networks (CNNs) has made a major contribution in the last few years. However, the image retrieval-based VPR becomes more challenging when the same places experience strong viewpoint and seasonal transitions. This thesis concentrates on improving the retrieval performance of VPR system, generally targeting the place correspondence.
Despite the remarkable performances of state-of-the-art deep CNNs for VPR, the significant computation- and memory-overhead limit their practical deployment for resource constrained mobile robots. This thesis investigates the utility of shallow CNNs for power-efficient VPR applications. The proposed VPR frameworks focus on novel image regions that can contribute in recognizing places under dubious environment and viewpoint variations.
Employing challenging place recognition benchmark datasets, this thesis further illustrates and evaluates the robustness of shallow CNN-based regional features against viewpoint and appearance changes coupled with dynamic instances, such as pedestrians, vehicles etc. Finally, the presented computation-efficient and light-weight VPR methodologies have shown boostup in matching performance in terms of Area under Precision-Recall curves (AUC-PR curves) over state-of-the-art deep neural network based place recognition and SLAM algorithms
Integrated Applications of Geo-Information in Environmental Monitoring
This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society
Advances in Automated Driving Systems
Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human–machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human–machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic
Sustainable Agriculture and Advances of Remote Sensing (Volume 2)
Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others
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