Deep Neuroevolution: Smart City Applications

Particularmente, la contribución de esta tesis se centra en cuatro aspectos: Primero, proponemos la técnica Mean Absolute Error Random Sampling (MRS) para estimar el rendimiento de una RNN, la cual se basa en la distribución del error observado en un muestreo aleatorio. Nuestros resultados muestran que MRS es una estimación fiable y de bajo coste computacional para predecir el rendimiento de una RNN. Segundo, diseñamos un algoritmo evolutivo (RESN) que explota MRS para optimizar la arquitectura de una RNN. RESN muestra resultados competitivos a la vez que reduce significativamente el tiempo. Tercero, en el contexto de la aplicación, proponemos soluciones para problemas de movilidad, electricidad y gestión de residuos inteligente, y hemos revisado el estado del arte de la ciudad inteligente y su relación con la informática. Cuarto, hemos desarrollado la biblioteca de software Deep Learning OPTimization (DLOPT), la cual está disponible bajo la licencia GNU GPL v3. Ésta contiene la mayor parte del trabajo realizado en esta tesis.El interés por desarrollar redes neuronales artificiales ha resurgido de la mano del Aprendizaje Profundo. En términos simples, el aprendizaje profundo consiste en diseñar y entrenar una red neuronal de gran complejidad y tamaño con una inmensa cantidad de datos. Esta creciente complejidad propone nuevos desafíos, siendo de especial relevancia la optimización del diseño dado un problema. Tradicionalmente, este problema ha sido resuelto en una combinación de conocimiento experto (humano) con prueba y error. Sin embargo, conforme la complejidad aumenta, este acercamiento se vuelve ineficiente (e impracticable). Esta tesis doctoral aborda el diseño de redes neuronales recurrentes (RNN), un tipo de red neuronal profunda, desde la neuroevolución. Concretamente, se combinan técnicas de aprendizaje automático con metaheurísticas avanzadas, con el fin de proveer una solución eficaz y eficiente. Por otra parte, se aplican las técnicas desarrolladas a problemas de la ciudad inteligente

MDAS: a new multimodal benchmark dataset for remote sensing

In Earth observation, multimodal data fusion is an intuitive strategy to break the limitation of individual data. Complementary physical contents of data sources allow comprehensive and precise information retrieval. With current satellite missions, such as ESA Copernicus programme, various data will be accessible at an affordable cost. Future applications will have many options for data sources. Such a privilege can be beneficial only if algorithms are ready to work with various data sources. However, current data fusion studies mostly focus on the fusion of two data sources. There are two reasons; first, different combinations of data sources face different scientific challenges. For example, the fusion of synthetic aperture radar (SAR) data and optical images needs to handle the geometric difference, while the fusion of hyperspectral and multispectral images deals with different resolutions on spatial and spectral domains. Second, nowadays, it is still both financially and labour expensive to acquire multiple data sources for the same region at the same time. In this paper, we provide the community with a benchmark multimodal data set, MDAS, for the city of Augsburg, Germany. MDAS includes synthetic aperture radar data, multispectral image, hyperspectral image, digital surface model (DSM), and geographic information system (GIS) data. All these data are collected on the same date, 7 May 2018. MDAS is a new benchmark data set that provides researchers rich options on data selections. In this paper, we run experiments for three typical remote sensing applications, namely, resolution enhancement, spectral unmixing, and land cover classification, on MDAS data set. Our experiments demonstrate the performance of representative state-of-the-art algorithms whose outcomes can serve as baselines for further studies. The dataset is publicly available at https://doi.org/10.14459/2022mp1657312 (Hu et al., 2022a) and the code (including the pre-trained models) at https://doi.org/10.5281/zenodo.7428215 (Hu et al., 2022b)

Random error sampling-based recurrent neural network architecture optimization

Recurrent neural networks are good at solving prediction problems. However, finding a network that suits a problem is quite hard because their performance is strongly affected by their architecture configuration. Automatic architecture optimization methods help to find the most suitable design, but they are not extensively adopted because of their high computational cost. In this work, we introduce the Random Error Sampling-based Neuroevolution (RESN), an evolutionary algorithm that uses the mean absolute error random sampling, a training-free approach to predict the expected performance of an artificial neural network, to optimize the architecture of a network. We empirically validate our proposal on four prediction problems, and compare our technique to training-based architecture optimization techniques, neuroevolutionary approaches, and expert designed solutions. Our findings show that we can achieve state-of-the-art error performance and that we reduce by half the time needed to perform the optimization

DENETHOR: The DynamicEarthNET dataset for Harmonized, inter-Operable, analysis-Ready, daily crop monitoring from space

Recent advances in remote sensing products allow near-real time monitoring of the Earth’s surface. Despite increasing availability of near-daily time-series of satellite imagery, there has been little exploration of deep learning methods to utilize the unprecedented temporal density of observations. This is particularly interesting in crop monitoring where time-series remote sensing data has been used frequently to exploit phenological differences of crops in the growing cycle over time. In this work, we present DENETHOR: The DynamicEarthNET dataset for Harmonized, inter-Operabel, analysis-Ready, daily crop monitoring from space. Our dataset contains daily, analysis-ready Planet Fusion data together with Sentinel-1 radar and Sentinel-2 optical time-series for crop type classification in Northern Germany. Our baseline experiments underline that incorporating the available spatial and temporal information fully may not be straightforward and could require the design of tailored architectures. The dataset presents two main challenges to the community: Exploit the temporal dimension for improved crop classification and ensure that models can handle a domain shift to a different year