7 research outputs found
IoT-based platform for automated IEQ spatio-temporal analysis in buildings using machine learning techniques
Financiaciado para publicación en acceso aberto: Universidade de Vigo/CISUGProviding accurate information about the indoor environmental quality (IEQ) conditions inside building spaces is
essential to assess the comfort levels of their occupants. These values may vary inside the same space, especially
for large zones, requiring many sensors to produce a fine-grained representation of the space conditions, which
increases hardware installation and maintenance costs. However, sound interpolation techniques may produce
accurate values with fewer input points, reducing the number of sensors needed. This work presents a platform to
automate this accurate IEQ representation based on a few sensor devices placed across a large building space. A
case study is presented in a research centre in Spain using 8 wall-mounted devices and an additional moving
device to train a machine learning model. The system yields accurate results for estimations at positions and
times never seen before by the trained model, with relative errors between 4% and 10% for the analysed
variables.Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C2Ministerio de Ciencia, Innovación y Universidades | Ref. FPU17/ 01834Ministerio de Ciencia, Innovación y Universidades | Ref. FPU19/01187Universidad de Vigo | Ref. 00VI 131H 641.0
Deep neural network for complex open-water wetland mapping using high-resolution WorldView-3 and airborne LiDAR data
Wetland inventory maps are essential information for the conservation and management of natural wetland areas. The classification framework is crucial for successful mapping of complex wetlands, including the model selection, input variables and training procedures. In this context, deep neural network (DNN) is a powerful technique for remote sensing image classification, but this model application for wetland mapping has not been discussed in the previous literature, especially using commercial WorldView-3 data. This study developed a new framework for wetland mapping using DNN algorithm and WorldView-3 image in the Millrace Flats Wildlife Management Area, Iowa, USA. The study area has several wetlands with a variety of shapes and sizes, and the minimum mapping unit was defined as 20 m2 (0.002 ha). A set of potential variables was derived from WorldView-3 and auxiliary LiDAR data, and a feature selection procedure using principal components analysis (PCA) was used to identify the most important variables for wetland classification. Furthermore, traditional machine learning methods (support vector machine, random forest and k-nearest neighbor) were also implemented for the comparison of results. In general, the results show that DNN achieved satisfactory results in the study area (overall accuracy = 93.33 %), and we observed a high spatial overlap between reference and classified wetland polygons (Jaccard index ∼0.8). Our results confirm that PCA-based feature selection was effective in the optimization of DNN performance, and vegetation and textural indices were the most informative variables. In addition, the comparison of results indicated that DNN classification achieved relatively similar accuracies to other methods. The total classification errors vary from 0.104 to 0.111 among the methods, and the overlapped areas between reference and classified polygons range between 87.93 and 93.33 %. Finally, the findings of this study have three main implications. First, the integration of DNN model and WorldView-3 image is useful for wetland mapping at 1.2-m, but DNN results did not outperform other methods in this study area. Second, the feature selection was important for model performance, and the combination of most relevant input parameters contributes to the success of all tested models. Third, the spatial resolution of WorldView-3 is appropriate to preserve the shape and extent of small wetlands, while the application of medium resolution image (30-m) has a negative impact on the accurate delineation of these areas. Since commercial satellite data are becoming more affordable for remote sensing users, this study provides a framework that can be utilized to integrate very high-resolution imagery and deep learning in the classification of complex wetland areas
Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images
Surface water mapping is essential for monitoring climate change, water resources, ecosystem services and the hydrological cycle. In this study, we adopt a multilayer perceptron (MLP) neural network to identify surface water in Landsat 8 satellite images. To evaluate the performance of the proposed method when extracting surface water, eight images of typical regions are collected, and a water index and support vector machine are employed for comparison. Through visual inspection and a quantitative index, the performance of the proposed algorithm in terms of the entire scene classification, various surface water types and noise suppression is comprehensively compared with those of the water index and support vector machine. Moreover, band optimization, image preprocessing and a training sample for the proposed algorithm are analyzed and discussed. We find that (1) based on the quantitative evaluation, the performance of the surface water extraction for the entire scene when using the MLP is better than that when using the water index or support vector machine. The overall accuracy of the MLP ranges from 98.25–100%, and the kappa coefficients of the MLP range from 0.965–1. (2) The MLP can precisely extract various surface water types and effectively suppress noise caused by shadows and ice/snow. (3) The 1–7-band composite provides a better band optimization strategy for the proposed algorithm, and image preprocessing and high-quality training samples can benefit from the accuracy of the classification. In future studies, the automation and universality of the proposed algorithm can be further enhanced with the generation of training samples based on newly-released global surface water products. Therefore, this method has the potential to map surface water based on Landsat series images or other high-resolution images and can be implemented for global surface water mapping, which will help us better understand our changing planet
Aplicação de técnicas de aprendizagem de máquinas na previsão de vertimento em usinas hidrelétricas
Operation at a hydroelectric plant is dependent on several factors such as the
schedule of power generation, the volume of water available in its reservoir, the conditions
of the river downstream, and the safety of the dams. A major challenge of the operation is
to control the spillage from the reservoir. Although the spillage action represents the loss of
energy resources, this action is also a powerful strategy to control the level of the reservoir,
ensuring the safety of the dam. Decision-making regarding this operation is carried out
in advance and is generally based on estimated level and demand information. In this
context, this work applies supervised machine learning techniques to predict, for five hours
to come, the operating condition of pouring in a hydroelectric plant. Intending to be used
in real-time, this method aims to assist the operator, so that he can make more assertive
and safer decisions, preserving energy resources and promoting increased safety of dams,
and consequently, of workers and the population that resides river banks downstream of
the plant. Random Forest, Multilayer Perceptron and the combination of these learning
algorithms are adopted and compared in this work. The proposed methodology was
implemented and tested with a hydroelectric plant located on the Tocantins River, Brazil,
with a generation capacity of 902.5MW. The results of the methodology demonstrated
that the tool has the capacity to be an efficient aid to the operators of a plant in decision
making, since the forecasting models reached levels above 99% of correctness in the spillage
forecasts.A operação de uma usina hidrelétrica é dependente de diversos fatores como a
programação de geração de energia, do volume de água disponível no seu reservatório, as
condições do rio a jusante e a segurança das barragens. Um grande desafio da operação é
controlar o vertimento da água do reservatório. Embora a ação de vertimento represente
a perda de recursos energéticos, esta ação também é uma estratégia poderosa para
controlar o nível do reservatório, garantindo a segurança da barragem. A tomada de
decisão quanto a essa operação é realizada com antecedência e geralmente se baseia em
informações estimadas de nível e demanda. Neste contexto, este trabalho aplica técnicas
de aprendizado supervisionado de máquina para predizer, cinco horas a frente, a condição
operativa de vertimento em uma usina hidrelétrica. Com o objetivo de ser utilizado em
tempo real, este método visa auxiliar o operador, de modo que este consiga tomar decisões
mais assertivas e seguras, preservando recursos energéticos e promovendo aumento da
segurança das barragens e, consequentemente, dos trabalhadores e da população que reside
às margens do rio a jusante da usina. Floresta Aleatória, Perceptron Multicamadas e a
combinação destes algoritmos de aprendizado são utilizados e comparados neste trabalho.
A metodologia proposta foi implementada e testada com uma usina hidrelétrica localizada
no Rio Tocantins, Brasil, com capacidade de geração de 902,5MW. Os resultados da
metodologia demonstraram ue a ferramenta tem capacidade de ser um auxílio eficiente
aos operadores de uma usina nas tomadas de decisão, visto que os modelos de previsão
alcançaram patamares superiores à 99% de acerto nas previsões de vertimento
Investigation of Coastal Vegetation Dynamics and Persistence in Response to Hydrologic and Climatic Events Using Remote Sensing
Coastal Wetlands (CW) provide numerous imperative functions and provide an economic base for human societies. Therefore, it is imperative to track and quantify both short and long-term changes in these systems. In this dissertation, CW dynamics related to hydro-meteorological signals were investigated using a series of LANDSAT-derived normalized difference vegetation index (NDVI) data and hydro-meteorological time-series data in Apalachicola Bay, Florida, from 1984 to 2015. NDVI in forested wetlands exhibited more persistence compared to that for scrub and emergent wetlands. NDVI fluctuations generally lagged temperature by approximately three months, and water level by approximately two months. This analysis provided insight into long-term CW dynamics in the Northern Gulf of Mexico. Long-term studies like this are dependent on optical remote sensing data such as Landsat which is frequently partially obscured due to clouds and this can that makes the time-series sparse and unusable during meteorologically active seasons. Therefore, a multi-sensor, virtual constellation method is proposed and demonstrated to recover the information lost due to cloud cover. This method, named Tri-Sensor Fusion (TSF), produces a simulated constellation for NDVI by integrating data from three compatible satellite sensors. The visible and near-infrared (VNIR) bands of Landsat-8 (L8), Sentinel-2, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were utilized to map NDVI and to compensate each satellite sensor\u27s shortcomings in visible coverage area. The quantitative comparison results showed a Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) of 0.0020 sr-1 and 0.88, respectively between true observed and fused L8 NDVI. Statistical test results and qualitative performance evaluation suggest that TSF was able to synthesize the missing pixels accurately in terms of the absolute magnitude of NDVI. The fusion improved the spatial coverage of CWs reasonably well and ultimately increases the continuity of NDVI data for long term studies
Deep learning for land cover and land use classification
Recent advances in sensor technologies have witnessed a vast amount of very fine spatial resolution (VFSR) remotely sensed imagery being collected on a daily basis. These VFSR images present fine spatial details that are spectrally and spatially complicated, thus posing huge challenges in automatic land cover (LC) and land use (LU) classification. Deep learning reignited the pursuit of artificial intelligence towards a general purpose machine to be able to perform any human-related tasks in an automated fashion. This is largely driven by the wave of excitement in deep machine learning to model the high-level abstractions through hierarchical feature representations without human-designed features or rules, which demonstrates great potential in identifying and characterising LC and LU patterns from VFSR imagery. In this thesis, a set of novel deep learning methods are developed for LC and LU image classification based on the deep convolutional neural networks (CNN) as an example. Several difficulties, however, are encountered when trying to apply the standard pixel-wise CNN for LC and LU classification using VFSR images, including geometric distortions, boundary uncertainties and huge computational redundancy. These technical challenges for LC classification were solved either using rule-based decision fusion or through uncertainty modelling using rough set theory. For land use, an object-based CNN method was proposed, in which each segmented object (a group of homogeneous pixels) was sampled and predicted by CNN with both within-object and between-object information. LU was, thus, classified with high accuracy and efficiency. Both LC and LU formulate a hierarchical ontology at the same geographical space, and such representations are modelled by their joint distribution, in which LC and LU are classified simultaneously through iteration. These developed deep learning techniques achieved by far the highest classification accuracy for both LC and LU, up to around 90% accuracy, about 5% higher than the existing deep learning methods, and 10% greater than traditional pixel-based and object-based approaches. This research made a significant contribution in LC and LU classification through deep learning based innovations, and has great potential utility in a wide range of geospatial applications