10 research outputs found

    Generative feature extraction from sentinel 1 and 2 data for prediction of forest aboveground biomass in the Italian Alps

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    —Aboveground biomass (AGB) is an important forest attribute directly linked to the forest carbon pool. The use of satellite remote sensing (RS) data has increased for AGB prediction due to their large footprint and low-cost availability. However, they have been limited due to saturation effect that leads to low prediction precision. In this article, we propose an innovative and dynamic architecture based on generative neural network that extracts target oriented generative features for predicting forest AGB using satellite RS data. These features are more resilient to mixed forest types and geographical conditions as compared to the traditional features and models. The effectiveness of the proposed features was assessed by experiments performed using multispectral, synthetic aperture radar, and combined dual-source datasets. The proposed model achieved best performance in terms of precision, model agreement, and overfitting as compared to the other conventional models for all analyzed datasets. The t-distributed stochastic neighbor embedding scatterplots of the generative features clearly show one dimension of the feature space associated with the target AGB. Feature importance analysis indicated that the produced generative features were more significant than the conventional analytical features. Also, the model provided a robust framework for homogeneous fusion of multisensor features from satellite RS data for predicting AG

    Realistic adversarial machine learning to improve network intrusion detection

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    Modern organizations can significantly benefit from the use of Artificial Intelligence (AI), and more specifically Machine Learning (ML), to tackle the growing number and increasing sophistication of cyber-attacks targeting their business processes. However, there are several technological and ethical challenges that undermine the trustworthiness of AI. One of the main challenges is the lack of robustness, which is an essential property to ensure that ML is used in a secure way. Improving robustness is no easy task because ML is inherently susceptible to adversarial examples: data samples with subtle perturbations that cause unexpected behaviors in ML models. ML engineers and security practitioners still lack the knowledge and tools to prevent such disruptions, so adversarial examples pose a major threat to ML and to the intelligent Network Intrusion Detection (NID) systems that rely on it. This thesis presents a methodology for a trustworthy adversarial robustness analysis of multiple ML models, and an intelligent method for the generation of realistic adversarial examples in complex tabular data domains like the NID domain: Adaptative Perturbation Pattern Method (A2PM). It is demonstrated that a successful adversarial attack is not guaranteed to be a successful cyber-attack, and that adversarial data perturbations can only be realistic if they are simultaneously valid and coherent, complying with the domain constraints of a real communication network and the class-specific constraints of a certain cyber-attack class. A2PM can be used for adversarial attacks, to iteratively cause misclassifications, and adversarial training, to perform data augmentation with slightly perturbed data samples. Two case studies were conducted to evaluate its suitability for the NID domain. The first verified that the generated perturbations preserved both validity and coherence in Enterprise and Internet-of Things (IoT) network scenarios, achieving realism. The second verified that adversarial training with simple perturbations enables the models to retain a good generalization to regular IoT network traffic flows, in addition to being more robust to adversarial examples. The key takeaway of this thesis is: ML models can be incredibly valuable to improve a cybersecurity system, but their own vulnerabilities must not be disregarded. It is essential to continue the research efforts to improve the security and trustworthiness of ML and of the intelligent systems that rely on it.Organizações modernas podem beneficiar significativamente do uso de Inteligência Artificial (AI), e mais especificamente Aprendizagem Automática (ML), para enfrentar a crescente quantidade e sofisticação de ciberataques direcionados aos seus processos de negócio. No entanto, há vários desafios tecnológicos e éticos que comprometem a confiabilidade da AI. Um dos maiores desafios é a falta de robustez, que é uma propriedade essencial para garantir que se usa ML de forma segura. Melhorar a robustez não é uma tarefa fácil porque ML é inerentemente suscetível a exemplos adversos: amostras de dados com perturbações subtis que causam comportamentos inesperados em modelos ML. Engenheiros de ML e profissionais de segurança ainda não têm o conhecimento nem asferramentas necessárias para prevenir tais disrupções, por isso os exemplos adversos representam uma grande ameaça a ML e aos sistemas de Deteção de Intrusões de Rede (NID) que dependem de ML. Esta tese apresenta uma metodologia para uma análise da robustez de múltiplos modelos ML, e um método inteligente para a geração de exemplos adversos realistas em domínios de dados tabulares complexos como o domínio NID: Método de Perturbação com Padrões Adaptativos (A2PM). É demonstrado que um ataque adverso bem-sucedido não é garantidamente um ciberataque bem-sucedido, e que as perturbações adversas só são realistas se forem simultaneamente válidas e coerentes, cumprindo as restrições de domínio de uma rede de computadores real e as restrições específicas de uma certa classe de ciberataque. A2PM pode ser usado para ataques adversos, para iterativamente causar erros de classificação, e para treino adverso, para realizar aumento de dados com amostras ligeiramente perturbadas. Foram efetuados dois casos de estudo para avaliar a sua adequação ao domínio NID. O primeiro verificou que as perturbações preservaram tanto a validade como a coerência em cenários de redes Empresariais e Internet-das-Coisas (IoT), alcançando o realismo. O segundo verificou que o treino adverso com perturbações simples permitiu aos modelos reter uma boa generalização a fluxos de tráfego de rede IoT, para além de serem mais robustos contra exemplos adversos. A principal conclusão desta tese é: os modelos ML podem ser incrivelmente valiosos para melhorar um sistema de cibersegurança, mas as suas próprias vulnerabilidades não devem ser negligenciadas. É essencial continuar os esforços de investigação para melhorar a segurança e a confiabilidade de ML e dos sistemas inteligentes que dependem de ML

    Using GOES-16 ABI data to detect convection, estimate latent heating, and initiate convection in a high resolution model

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    2021 Spring.Includes bibliographical references.Convective-scale data assimilation has received more attention in recent years as spatial resolution of forecast models has become finer and more observation data are available at such fine scale. Significant amounts of observation data are available over the globe, but only a limited number of observations are assimilated in operational forecast models in the most effective way. One of the most important observation data for predicting precipitation is radar reflectivity from ground-based radars as it provides three-dimensional structure of precipitation. Many operational models use these data to create cloud analysis and initiate convection. In High-Resolution Rapid Refresh (HRRR), the cloud permitting operational model at National Oceanic and Atmospheric Administration (NOAA) that is responsible for short term forecasts over the Contiguous United States (CONUS), latent heating is derived from ground-based radars and added in the observed convective regions to initiate convection. Even though adding heating is shown to improve forecasts of convection, this cannot be done over ocean or mountainous regions where radar data is not available. Geostationary data are available regardless of radar coverage and its data are provided in similar spatial and temporal resolution as ground-based radar. Currently, geostationary data are only used as a source of cloud top information or atmospheric motion vectors due to lack of vertical information. However, Geostationary Operational Environmental Satellites (GOES)-16 and -17 have high temporal resolution data that can compensate the lack of vertical information. From loops of one-minute visible images, convective clouds can be detected by finding a region with a constant bubbling. Therefore, this dissertation seeks a way to use these high temporal resolution GOES-16 data to mimic what radars do over land. In the first two papers presented in the dissertation, two methods are proposed to detect convection using one-minute GOES-16 Advanced Baseline Imager (ABI) data. The first method explicitly calculates Tb decrease or lumpiness of reflectance data and finds convective regions. The second paper tries to automate this process using machine learning method. Results from both methods are comparable to radar product, but the machine learning model seems to detect more convective regions than the conventional method. In the third paper, latent heating profiles for convective clouds are estimated from GOES-16. Once a convective cloud is detected, latent heating profiles corresponding to cloud top temperature of the convective cloud is searched from the lookup table created using model simulations. This technique is similar to spaceborne radar inferred latent heating developed for National Aeronautics and Space Administration (NASA)'s Global Precipitation Measurement Mission (GPM). Latent heating assigned from GOES-16 is shown to be similar to latent heating derived from Next-Generation Radar (NEXRAD) once they are summed up over each cloud. Finally in the last paper, latent heating estimated by using the method from the third paper are assimilated into the Weather Research and Forecasting (WRF) model to examine impacts of using GOES-16 derived latent heating in initiating convection in the forecast model. Two case studies are presented to compare results using GOES-16 derived heating and NEXRAD derived heating. Results show that using GOES-16 derived heating sometimes produce deeper convection than it should, but it improves overall precipitation forecasts. This appears related to the much deeper column of heating assigned by GOES than the empirical relation used by the HRRR operational scheme. In addition, in a case when storms developed over Gulf of Mexico where radar data are not available, forecasts are improved using GOES-16 latent heating

    Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN

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    In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The network is trained using the non-saturating conditional Generative Adversarial Network (cGAN) and Mean Squared Error (MSE) loss terms to generate results that better learn the complex distribution of precipitation in the observed data. Common verification metrics such as Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Bias, Correlation and MSE are used to evaluate the accuracy of both R/NR classification and real-valued precipitation estimates. Statistics and visualizations of the evaluation measures show improvements in the precipitation retrieval accuracy in the proposed framework compared to the baseline models trained using conventional MSE loss terms. This framework is proposed as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network- Cloud Classification System) algorithm for estimating global precipitation

    Conditional generative adversarial networks (cGANs) for near real-time precipitation estimation from multispectral GOES-16 satellite imageries-PERSIANN-cGAN

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    In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The network is trained using the non-saturating conditional Generative Adversarial Network (cGAN) and Mean Squared Error (MSE) loss terms to generate results that better learn the complex distribution of precipitation in the observed data. Common verification metrics such as Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Bias, Correlation and MSE are used to evaluate the accuracy of both R/NR classification and real-valued precipitation estimates. Statistics and visualizations of the evaluation measures show improvements in the precipitation retrieval accuracy in the proposed framework compared to the baseline models trained using conventional MSE loss terms. This framework is proposed as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network- Cloud Classification System) algorithm for estimating global precipitation

    Elephant space use in relation to ephemeral surface water availability in the eastern Okavango Panhandle, Botswana

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    The movement and distribution of elephants can be influenced by environmental factors over time (Foley, 2002). Examining how features in the landscape such as vegetation productivity, water sources and anthropogenic activities drive the movement of elephants can help in understanding patterns of movement. It can also help to inform the establishment and alignment of protected areas, wildlife corridors and identification of tourism hotspots as well as policy interventions to manage Human-Elephant Conflict (HEC). The Okavango Panhandle in Botswana is a HEC hotspot and the focus of My study. A number of strategies to address HEC are underway in the area, however one longer term strategy that has been proposed in this area involves provision of artificial water sources to influence elephant movements and keep animals away from fields during the cropping season. However, an improved understanding of how elephants utilize their habitats in relation to natural ephemeral surface water and other factors that influence their movements from dryland habitats to the Okavango Delta resources is needed to inform such management decisions. My study seeks to establish the role of ephemeral surface water on elephant distribution in the eastern Okavango Panhandle, Botswana as well as assess the movement distribution of elephants in relation to the seasonality, proximity and spatial extent of water presence represented by ephemeral surface water. Time series analysis of water extent on ephemeral surface water of the eastern Okavango panhandle will be developed and overlaid with elephant movement datasets. Elephant collar data from 15 elephants (5 males and 10 females) in the eastern Okavango Panhandle, Botswana have been analysed and Home Range (HR) sizes estimated using Kernel Density Estimation (KDE). The relative importance/probability of environmental variables in determining elephants' movement based on the Utilization Distribution (UD) were computed using Generalized Linear Mixed Models (GLMMs). I utilized a remote sensing spectral index, namely the Automated Water Extraction Index (AWEI) to delineate ephemeral surface water in dryland (excluding permanent waters) of the study area. The results reveal that during the wet season, elephants were evenly spread out all over the study area until the early dry season (April-June) when the ephemeral waterholes dried up. Elephants moved southwards towards the permanent waters of the Okavango River, where there are many human settlements and farms. Male HR sizes were found to be bigger than those of female elephants. Wet season (early and late) home range sizes were also bigger when compared to dry season (early and late) HR size. Mean daily distances were computed to investigate the effect of season on elephant daily distances and the distances ranged between 5km and 6.8km in the late wet and in the early wet and late dry season respectively. The Resource Selection Function (RSF) analysis shows that water adjacent sites are preferred over distant ones and both sexes prefer areas with high NDVI, with this preference being more pronounced in males. The seasonal variation of water use is notable in that it affirms the importance of proximity to water for elephants and has implications for their management and HEC. For example, I found that ephemeral surface water has a significant role in influencing elephant spatial use in the area, particularly during the early and late wet season. As ephemeral pans dried and NDVI (vegetation greenness) decreased, elephants started to move closer to the Okavango Delta and consequently human settlements and fields. However, further investigations into the timing of movements away from ephemeral waterholes and the influence of other environmental factors on elephant movements in the area would be needed before any recommendations can be made regarding artificial water provision in this area
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