Query Processing In Location-based Services

With the advances in wireless communication technology and advanced positioning systems, a variety of Location-Based Services (LBS) become available to the public. Mobile users can issue location-based queries to probe their surrounding environments. One important type of query in LBS is moving monitoring queries over mobile objects. Due to the high frequency in location updates and the expensive cost of continuous query processing, server computation capacity and wireless communication bandwidth are the two limiting factors for large-scale deployment of moving object database systems. To address both of the scalability factors, distributed computing has been considered. These schemes enable moving objects to participate as a peer in query processing to substantially reduce the demand on server computation, and wireless communications associated with location updates. In the first part of this dissertation, we propose a distributed framework to process moving monitoring queries over moving objects in a spatial network environment. In the second part of this dissertation, in order to reduce the communication cost, we leverage both on-demand data access and periodic broadcast to design a new hybrid distributed solution for moving monitoring queries in an open space environment. Location-based services make our daily life more convenient. However, to receive the services, one has to reveal his/her location and query information when issuing locationbased queries. This could lead to privacy breach if these personal information are possessed by some untrusted parties. In the third part of this dissertation, we introduce a new privacy protection measure called query l-diversity, and provide two cloaking algorithms to achieve both location kanonymity and query l-diversity to better protect user privacy. In the fourth part of this dissertation, we design a hybrid three-tier architecture to help reduce privacy exposure. In the fifth part of this dissertation, we propose to use Road Network Embedding technique to process privacy protected queries

Service Abstractions for Scalable Deep Learning Inference at the Edge

Deep learning driven intelligent edge has already become a reality, where millions of mobile, wearable, and IoT devices analyze real-time data and transform those into actionable insights on-device. Typical approaches for optimizing deep learning inference mostly focus on accelerating the execution of individual inference tasks, without considering the contextual correlation unique to edge environments and the statistical nature of learning-based computation. Specifically, they treat inference workloads as individual black boxes and apply canonical system optimization techniques, developed over the last few decades, to handle them as yet another type of computation-intensive applications. As a result, deep learning inference on edge devices still face the ever increasing challenges of customization to edge device heterogeneity, fuzzy computation redundancy between inference tasks, and end-to-end deployment at scale. In this thesis, we propose the first framework that automates and scales the end-to-end process of deploying efficient deep learning inference from the cloud to heterogeneous edge devices. The framework consists of a series of service abstractions that handle DNN model tailoring, model indexing and query, and computation reuse for runtime inference respectively. Together, these services bridge the gap between deep learning training and inference, eliminate computation redundancy during inference execution, and further lower the barrier for deep learning algorithm and system co-optimization. To build efficient and scalable services, we take a unique algorithmic approach of harnessing the semantic correlation between the learning-based computation. Rather than viewing individual tasks as isolated black boxes, we optimize them collectively in a white box approach, proposing primitives to formulate the semantics of the deep learning workloads, algorithms to assess their hidden correlation (in terms of the input data, the neural network models, and the deployment trials) and merge common processing steps to minimize redundancy

Searching and mining in enriched geo-spatial data

The emergence of new data collection mechanisms in geo-spatial applications paired with a heightened tendency of users to volunteer information provides an ever-increasing flow of data of high volume, complex nature, and often associated with inherent uncertainty. Such mechanisms include crowdsourcing, automated knowledge inference, tracking, and social media data repositories. Such data bearing additional information from multiple sources like probability distributions, text or numerical attributes, social context, or multimedia content can be called multi-enriched. Searching and mining this abundance of information holds many challenges, if all of the data's potential is to be released. This thesis addresses several major issues arising in that field, namely path queries using multi-enriched data, trend mining in social media data, and handling uncertainty in geo-spatial data. In all cases, the developed methods have made significant contributions and have appeared in or were accepted into various renowned international peer-reviewed venues. A common use of geo-spatial data is path queries in road networks where traditional methods optimise results based on absolute and ofttimes singular metrics, i.e., finding the shortest paths based on distance or the best trade-off between distance and travel time. Integrating additional aspects like qualitative or social data by enriching the data model with knowledge derived from sources as mentioned above allows for queries that can be issued to fit a broader scope of needs or preferences. This thesis presents two implementations of incorporating multi-enriched data into road networks. In one case, a range of qualitative data sources is evaluated to gain knowledge about user preferences which is subsequently matched with locations represented in a road network and integrated into its components. Several methods are presented for highly customisable path queries that incorporate a wide spectrum of data. In a second case, a framework is described for resource distribution with reappearance in road networks to serve one or more clients, resulting in paths that provide maximum gain based on a probabilistic evaluation of available resources. Applications for this include finding parking spots. Social media trends are an emerging research area giving insight in user sentiment and important topics. Such trends consist of bursts of messages concerning a certain topic within a time frame, significantly deviating from the average appearance frequency of the same topic. By investigating the dissemination of such trends in space and time, this thesis presents methods to classify trend archetypes to predict future dissemination of a trend. Processing and querying uncertain data is particularly demanding given the additional knowledge required to yield results with probabilistic guarantees. Since such knowledge is not always available and queries are not easily scaled to larger datasets due to the #P-complete nature of the problem, many existing approaches reduce the data to a deterministic representation of its underlying model to eliminate uncertainty. However, data uncertainty can also provide valuable insight into the nature of the data that cannot be represented in a deterministic manner. This thesis presents techniques for clustering uncertain data as well as query processing, that take the additional information from uncertainty models into account while preserving scalability using a sampling-based approach, while previous approaches could only provide one of the two. The given solutions enable the application of various existing clustering techniques or query types to a framework that manages the uncertainty.Das Erscheinen neuer Methoden zur Datenerhebung in räumlichen Applikationen gepaart mit einer erhöhten Bereitschaft der Nutzer, Daten über sich preiszugeben, generiert einen stetig steigenden Fluss von Daten in großer Menge, komplexer Natur, und oft gepaart mit inhärenter Unsicherheit. Beispiele für solche Mechanismen sind Crowdsourcing, automatisierte Wissensinferenz, Tracking, und Daten aus sozialen Medien. Derartige Daten, angereichert mit mit zusätzlichen Informationen aus verschiedenen Quellen wie Wahrscheinlichkeitsverteilungen, Text- oder numerische Attribute, sozialem Kontext, oder Multimediainhalten, werden als multi-enriched bezeichnet. Suche und Datamining in dieser weiten Datenmenge hält viele Herausforderungen bereit, wenn das gesamte Potenzial der Daten genutzt werden soll. Diese Arbeit geht auf mehrere große Fragestellungen in diesem Feld ein, insbesondere Pfadanfragen in multi-enriched Daten, Trend-mining in Daten aus sozialen Netzwerken, und die Beherrschung von Unsicherheit in räumlichen Daten. In all diesen Fällen haben die entwickelten Methoden signifikante Forschungsbeiträge geleistet und wurden veröffentlicht oder angenommen zu diversen renommierten internationalen, von Experten begutachteten Konferenzen und Journals. Ein gängiges Anwendungsgebiet räumlicher Daten sind Pfadanfragen in Straßennetzwerken, wo traditionelle Methoden die Resultate anhand absoluter und oft auch singulärer Maße optimieren, d.h., der kürzeste Pfad in Bezug auf die Distanz oder der beste Kompromiss zwischen Distanz und Reisezeit. Durch die Integration zusätzlicher Aspekte wie qualitativer Daten oder Daten aus sozialen Netzwerken als Anreicherung des Datenmodells mit aus diesen Quellen abgeleitetem Wissen werden Anfragen möglich, die ein breiteres Spektrum an Anforderungen oder Präferenzen erfüllen. Diese Arbeit präsentiert zwei Ansätze, solche multi-enriched Daten in Straßennetze einzufügen. Zum einen wird eine Reihe qualitativer Datenquellen ausgewertet, um Wissen über Nutzerpräferenzen zu generieren, welches darauf mit Örtlichkeiten im Straßennetz abgeglichen und in das Netz integriert wird. Diverse Methoden werden präsentiert, die stark personalisierbare Pfadanfragen ermöglichen, die ein weites Spektrum an Daten mit einbeziehen. Im zweiten Fall wird ein Framework präsentiert, das eine Ressourcenverteilung im Straßennetzwerk modelliert, bei der einmal verbrauchte Ressourcen erneut auftauchen können. Resultierende Pfade ergeben einen maximalen Ertrag basieren auf einer probabilistischen Evaluation der verfügbaren Ressourcen. Eine Anwendung ist die Suche nach Parkplätzen. Trends in sozialen Medien sind ein entstehendes Forscchungsgebiet, das Einblicke in Benutzerverhalten und wichtige Themen zulässt. Solche Trends bestehen aus großen Mengen an Nachrichten zu einem bestimmten Thema innerhalb eines Zeitfensters, so dass die Auftrittsfrequenz signifikant über den durchschnittlichen Level liegt. Durch die Untersuchung der Fortpflanzung solcher Trends in Raum und Zeit präsentiert diese Arbeit Methoden, um Trends nach Archetypen zu klassifizieren und ihren zukünftigen Weg vorherzusagen. Die Anfragebearbeitung und Datamining in unsicheren Daten ist besonders herausfordernd, insbesondere im Hinblick auf das notwendige Zusatzwissen, um Resultate mit probabilistischen Garantien zu erzielen. Solches Wissen ist nicht immer verfügbar und Anfragen lassen sich aufgrund der \P-Vollständigkeit des Problems nicht ohne Weiteres auf größere Datensätze skalieren. Dennoch kann Datenunsicherheit wertvollen Einblick in die Struktur der Daten liefern, der mit deterministischen Methoden nicht erreichbar wäre. Diese Arbeit präsentiert Techniken zum Clustering unsicherer Daten sowie zur Anfragebearbeitung, die die Zusatzinformation aus dem Unsicherheitsmodell in Betracht ziehen, jedoch gleichzeitig die Skalierbarkeit des Ansatzes auf große Datenmengen sicherstellen

Big Data Mining to Construct Truck Tours

Cross-Border shipping of goods among different distributors is an essential part of transportation across Canada and U.S. These two countries are heavily dependent on border crossing locations to facilitate international trade between each other. This research considers the identification of the international tours accomplishing the shipping of goods. A truck tour is a round trip where a truck starts its journey from its firm or an industry, performing stops for different purposes that include taking a rest, fuel refilling, and transferring goods to multiple locations, and returns back to its initial firm location. In this thesis, we present a three step method on mining GPS truck data to identify all possible truck tours belonging to different carriers. In the first step, a clustering technique is applied on the stop locations to discover the firm for each carrier. A modified DBSCAN algorithm is proposed to achieve this task by automatically determining the two input parameters based on the data points provided. Various statistical measures like count of unique trucks and count of truck visits are applied on the resulting clusters to identify the firms of the respective carriers. In the second step, we tackle the problem of classifying the stop locations into two types: primary stops, where goods are transferred, and secondary stops like rest stations, where vehicle and driver needs are met. This problem is solved using one of the trade indicator called Specialization Index. Moreover, several set of features are explored to build the classification model to classify the type of stop locations. In the third step, having identified the firm, primary and secondary locations, an automated path finder is developed to identify the truck tours starting from each firm. The results of the specialization index and the feature-based classification in identifying stop events are compared with the entropy index from previous work. Experimental results show that the proposed set of cluster features significantly add classification power to our model giving 98.79% accuracy which in turn helps in discovering accurate tours

A Data-driven, High-performance and Intelligent CyberInfrastructure to Advance Spatial Sciences

abstract: In the field of Geographic Information Science (GIScience), we have witnessed the unprecedented data deluge brought about by the rapid advancement of high-resolution data observing technologies. For example, with the advancement of Earth Observation (EO) technologies, a massive amount of EO data including remote sensing data and other sensor observation data about earthquake, climate, ocean, hydrology, volcano, glacier, etc., are being collected on a daily basis by a wide range of organizations. In addition to the observation data, human-generated data including microblogs, photos, consumption records, evaluations, unstructured webpages and other Volunteered Geographical Information (VGI) are incessantly generated and shared on the Internet. Meanwhile, the emerging cyberinfrastructure rapidly increases our capacity for handling such massive data with regard to data collection and management, data integration and interoperability, data transmission and visualization, high-performance computing, etc. Cyberinfrastructure (CI) consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high-performance networks to improve research productivity and enable breakthroughs that are not otherwise possible. The Geospatial CI (GCI, or CyberGIS), as the synthesis of CI and GIScience has inherent advantages in enabling computationally intensive spatial analysis and modeling (SAM) and collaborative geospatial problem solving and decision making. This dissertation is dedicated to addressing several critical issues and improving the performance of existing methodologies and systems in the field of CyberGIS. My dissertation will include three parts: The first part is focused on developing methodologies to help public researchers find appropriate open geo-spatial datasets from millions of records provided by thousands of organizations scattered around the world efficiently and effectively. Machine learning and semantic search methods will be utilized in this research. The second part develops an interoperable and replicable geoprocessing service by synthesizing the high-performance computing (HPC) environment, the core spatial statistic/analysis algorithms from the widely adopted open source python package – Python Spatial Analysis Library (PySAL), and rich datasets acquired from the first research. The third part is dedicated to studying optimization strategies for feature data transmission and visualization. This study is intended for solving the performance issue in large feature data transmission through the Internet and visualization on the client (browser) side. Taken together, the three parts constitute an endeavor towards the methodological improvement and implementation practice of the data-driven, high-performance and intelligent CI to advance spatial sciences.Dissertation/ThesisDoctoral Dissertation Geography 201

PPMExplorer: Using Information Retrieval, Computer Vision and Transfer Learning Methods to Index and Explore Images of Pompeii

In this dissertation, we present and analyze the technology used in the making of PPMExplorer: Search, Find, and Explore Pompeii. PPMExplorer is a software tool made with data extracted from the Pompei: Pitture e Mosaic (PPM) volumes. PPM is a valuable set of volumes containing 20,000 historical annotated images of the archaeological site of Pompeii, Italy accompanied by extensive captions. We transformed the volumes from paper, to digital, to searchable. PPMExplorer enables archaeologist researchers to conduct and check hypotheses on historical findings. We present a theory that such a concept is possible by leveraging computer generated correlations between artifacts using image data, text data, and a combination of both. The acquisition and interconnection of the data are proposed and executed using image processing, natural language processing, data mining, and machine learning methods

Dynamic group trip planning queries in spatial databases

Trip planning queries are considered an integral part of Location Based Services. The advancement of positioning devices and highly available internet facilities enable users to access network information from anywhere at any time. In our research, we investigated Sequential Group Trip Planning (SGTP) queries. Given a set of starting and destination locations and an ordered sequence of Categories of Interests (COIs) for a group of users, a SGTP query returns the route for each user from their respective start and destination locations that minimizes the overall travel distance. We propose two approaches: Dynamic Group Trip Planning (DGTP) and Modified Dynamic Group Trip Planning (M-DGTP). The proposed DGTP approach enables users to plan a group trip in a more flexible manner and the M-DGTP approach optimizes the total travel distance of the group. We compare the results of our proposed strategies with an existing strategy called N-DGTP through experimental evaluation.School of Graduate Studies (SGS) of the University of Lethbridg

Towards an Efficient, Scalable Stream Query Operator Framework for Representing and Analyzing Continuous Fields

Advancements in sensor technology have made it less expensive to deploy massive numbers of sensors to observe continuous geographic phenomena at high sample rates and stream live sensor observations. This fact has raised new challenges since sensor streams have pushed the limits of traditional geo-sensor data management technology. Data Stream Engines (DSEs) provide facilities for near real-time processing of streams, however, algorithms supporting representing and analyzing Spatio-Temporal (ST) phenomena are limited. This dissertation investigates near real-time representation and analysis of continuous ST phenomena, observed by large numbers of mobile, asynchronously sampling sensors, using a DSE and proposes two novel stream query operator frameworks. First, the ST Interpolation Stream Query Operator Framework (STI-SQO framework) continuously transforms sensor streams into rasters using a novel set of stream query operators that perform ST-IDW interpolation. A key component of the STI-SQO framework is the 3D, main memory-based, ST Grid Index that enables high performance ST insertion and deletion of massive numbers of sensor observations through Isotropic Time Cell and Time Block-based partitioning. The ST Grid Index facilitates fast ST search for samples using ST shell-based neighborhood search templates, namely the Cylindrical Shell Template and Nested Shell Template. Furthermore, the framework contains the stream-based ST-IDW algorithms ST Shell and ST ak-Shell for high performance, parallel grid cell interpolation. Secondly, the proposed ST Predicate Stream Query Operator Framework (STP-SQO framework) efficiently evaluates value predicates over ST streams of ST continuous phenomena. The framework contains several stream-based predicate evaluation algorithms, including Region-Growing, Tile-based, and Phenomenon-Aware algorithms, that target predicate evaluation to regions with seed points and minimize the number of raster cells that are interpolated when evaluating value predicates. The performance of the proposed frameworks was assessed with regard to prediction accuracy of output results and runtime. The STI-SQO framework achieved a processing throughput of 250,000 observations in 2.5 s with a Normalized Root Mean Square Error under 0.19 using a 500×500 grid. The STP-SQO framework processed over 250,000 observations in under 0.25 s for predicate results covering less than 40% of the observation area, and the Scan Line Region Growing algorithm was consistently the fastest algorithm tested

An uncertainty prediction approach for active learning - application to earth observation

Mapping land cover and land usage dynamics are crucial in remote sensing since farmers are encouraged to either intensify or extend crop use due to the ongoing rise in the world’s population. A major issue in this area is interpreting and classifying a scene captured in high-resolution satellite imagery. Several methods have been put forth, including neural networks which generate data-dependent models (i.e. model is biased toward data) and static rule-based approaches with thresholds which are limited in terms of diversity(i.e. model lacks diversity in terms of rules). However, the problem of having a machine learning model that, given a large amount of training data, can classify multiple classes over different geographic Sentinel-2 imagery that out scales existing approaches remains open. On the other hand, supervised machine learning has evolved into an essential part of many areas due to the increasing number of labeled datasets. Examples include creating classifiers for applications that recognize images and voices, anticipate traffic, propose products, act as a virtual personal assistant and detect online fraud, among many more. Since these classifiers are highly dependent from the training datasets, without human interaction or accurate labels, the performance of these generated classifiers with unseen observations is uncertain. Thus, researchers attempted to evaluate a number of independent models using a statistical distance. However, the problem of, given a train-test split and classifiers modeled over the train set, identifying a prediction error using the relation between train and test sets remains open. Moreover, while some training data is essential for supervised machine learning, what happens if there is insufficient labeled data? After all, assigning labels to unlabeled datasets is a time-consuming process that may need significant expert human involvement. When there aren’t enough expert manual labels accessible for the vast amount of openly available data, active learning becomes crucial. However, given a large amount of training and unlabeled datasets, having an active learning model that can reduce the training cost of the classifier and at the same time assist in labeling new data points remains an open problem. From the experimental approaches and findings, the main research contributions, which concentrate on the issue of optical satellite image scene classification include: building labeled Sentinel-2 datasets with surface reflectance values; proposal of machine learning models for pixel-based image scene classification; proposal of a statistical distance based Evidence Function Model (EFM) to detect ML models misclassification; and proposal of a generalised sampling approach for active learning that, together with the EFM enables a way of determining the most informative examples. Firstly, using a manually annotated Sentinel-2 dataset, Machine Learning (ML) models for scene classification were developed and their performance was compared to Sen2Cor the reference package from the European Space Agency – a micro-F1 value of 84% was attained by the ML model, which is a significant improvement over the corresponding Sen2Cor performance of 59%. Secondly, to quantify the misclassification of the ML models, the Mahalanobis distance-based EFM was devised. This model achieved, for the labeled Sentinel-2 dataset, a micro-F1 of 67.89% for misclassification detection. Lastly, EFM was engineered as a sampling strategy for active learning leading to an approach that attains the same level of accuracy with only 0.02% of the total training samples when compared to a classifier trained with the full training set. With the help of the above-mentioned research contributions, we were able to provide an open-source Sentinel-2 image scene classification package which consists of ready-touse Python scripts and a ML model that classifies Sentinel-2 L1C images generating a 20m-resolution RGB image with the six studied classes (Cloud, Cirrus, Shadow, Snow, Water, and Other) giving academics a straightforward method for rapidly and effectively classifying Sentinel-2 scene images. Additionally, an active learning approach that uses, as sampling strategy, the observed prediction uncertainty given by EFM, will allow labeling only the most informative points to be used as input to build classifiers; Sumário: Uma Abordagem de Previsão de Incerteza para Aprendizagem Ativa – Aplicação à Observação da Terra O mapeamento da cobertura do solo e a dinâmica da utilização do solo são cruciais na deteção remota uma vez que os agricultores são incentivados a intensificar ou estender as culturas devido ao aumento contínuo da população mundial. Uma questão importante nesta área é interpretar e classificar cenas capturadas em imagens de satélite de alta resolução. Várias aproximações têm sido propostas incluindo a utilização de redes neuronais que produzem modelos dependentes dos dados (ou seja, o modelo é tendencioso em relação aos dados) e aproximações baseadas em regras que apresentam restrições de diversidade (ou seja, o modelo carece de diversidade em termos de regras). No entanto, a criação de um modelo de aprendizagem automática que, dada uma uma grande quantidade de dados de treino, é capaz de classificar, com desempenho superior, as imagens do Sentinel-2 em diferentes áreas geográficas permanece um problema em aberto. Por outro lado, têm sido utilizadas técnicas de aprendizagem supervisionada na resolução de problemas nas mais diversas áreas de devido à proliferação de conjuntos de dados etiquetados. Exemplos disto incluem classificadores para aplicações que reconhecem imagem e voz, antecipam tráfego, propõem produtos, atuam como assistentes pessoais virtuais e detetam fraudes online, entre muitos outros. Uma vez que estes classificadores são fortemente dependente do conjunto de dados de treino, sem interação humana ou etiquetas precisas, o seu desempenho sobre novos dados é incerta. Neste sentido existem propostas para avaliar modelos independentes usando uma distância estatística. No entanto, o problema de, dada uma divisão de treino-teste e um classificador, identificar o erro de previsão usando a relação entre aqueles conjuntos, permanece aberto. Mais ainda, embora alguns dados de treino sejam essenciais para a aprendizagem supervisionada, o que acontece quando a quantidade de dados etiquetados é insuficiente? Afinal, atribuir etiquetas é um processo demorado e que exige perícia, o que se traduz num envolvimento humano significativo. Quando a quantidade de dados etiquetados manualmente por peritos é insuficiente a aprendizagem ativa torna-se crucial. No entanto, dada uma grande quantidade dados de treino não etiquetados, ter um modelo de aprendizagem ativa que reduz o custo de treino do classificador e, ao mesmo tempo, auxilia a etiquetagem de novas observações permanece um problema em aberto. A partir das abordagens e estudos experimentais, as principais contribuições deste trabalho, que se concentra na classificação de cenas de imagens de satélite óptico incluem: criação de conjuntos de dados Sentinel-2 etiquetados, com valores de refletância de superfície; proposta de modelos de aprendizagem automática baseados em pixels para classificação de cenas de imagens de satétite; proposta de um Modelo de Função de Evidência (EFM) baseado numa distância estatística para detetar erros de classificação de modelos de aprendizagem; e proposta de uma abordagem de amostragem generalizada para aprendizagem ativa que, em conjunto com o EFM, possibilita uma forma de determinar os exemplos mais informativos. Em primeiro lugar, usando um conjunto de dados Sentinel-2 etiquetado manualmente, foram desenvolvidos modelos de Aprendizagem Automática (AA) para classificação de cenas e seu desempenho foi comparado com o do Sen2Cor – o produto de referência da Agência Espacial Europeia – tendo sido alcançado um valor de micro-F1 de 84% pelo classificador, o que representa uma melhoria significativa em relação ao desempenho Sen2Cor correspondente, de 59%. Em segundo lugar, para quantificar o erro de classificação dos modelos de AA, foi concebido o Modelo de Função de Evidência baseado na distância de Mahalanobis. Este modelo conseguiu, para o conjunto de dados etiquetado do Sentinel-2 um micro-F1 de 67,89% na deteção de classificação incorreta. Por fim, o EFM foi utilizado como uma estratégia de amostragem para a aprendizagem ativa, uma abordagem que permitiu atingir o mesmo nível de desempenho com apenas 0,02% do total de exemplos de treino quando comparado com um classificador treinado com o conjunto de treino completo. Com a ajuda das contribuições acima mencionadas, foi possível desenvolver um pacote de código aberto para classificação de cenas de imagens Sentinel-2 que, utilizando num conjunto de scripts Python, um modelo de classificação, e uma imagem Sentinel-2 L1C, gera a imagem RGB correspondente (com resolução de 20m) com as seis classes estudadas (Cloud, Cirrus, Shadow, Snow, Water e Other), disponibilizando à academia um método direto para a classificação de cenas de imagens do Sentinel-2 rápida e eficaz. Além disso, a abordagem de aprendizagem ativa que usa, como estratégia de amostragem, a deteção de classificacão incorreta dada pelo EFM, permite etiquetar apenas os pontos mais informativos a serem usados como entrada na construção de classificadores