Benchmarking Anchor-Based and Anchor-Free State-of-the-Art Deep Learning Methods for Individual Tree Detection in RGB High-Resolution Images

rban forests contribute to maintaining livability and increase the resilience of cities in the face of population growth and climate change. Information about the geographical distribution of individual trees is essential for the proper management of these systems. RGB high-resolution aerial images have emerged as a cheap and efficient source of data, although detecting and mapping single trees in an urban environment is a challenging task. Thus, we propose the evaluation of novel methods for single tree crown detection, as most of these methods have not been investigated in remote sensing applications. A total of 21 methods were investigated, including anchor-based (one and two-stage) and anchor-free state-of-the-art deep-learning methods. We used two orthoimages divided into 220 non-overlapping patches of 512 × 512 pixels with a ground sample distance (GSD) of 10 cm. The orthoimages were manually annotated, and 3382 single tree crowns were identified as the ground-truth. Our findings show that the anchor-free detectors achieved the best average performance with an AP50 of 0.686. We observed that the two-stage anchor-based and anchor-free methods showed better performance for this task, emphasizing the FSAF, Double Heads, CARAFE, ATSS, and FoveaBox models. RetinaNet, which is currently commonly applied in remote sensing, did not show satisfactory performance, and Faster R-CNN had lower results than the best methods but with no statistically significant difference. Our findings contribute to a better understanding of the performance of novel deep-learning methods in remote sensing applications and could be used as an indicator of the most suitable methods in such applications

A Review on Deep Learning in UAV Remote Sensing

Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms' applications have been conducted in an attempt to summarize the amount of information produced in its subfields. Recently, Unmanned Aerial Vehicles (UAV) based applications have dominated aerial sensing research. However, a literature revision that combines both "deep learning" and "UAV remote sensing" thematics has not yet been conducted. The motivation for our work was to present a comprehensive review of the fundamentals of Deep Learning (DL) applied in UAV-based imagery. We focused mainly on describing classification and regression techniques used in recent applications with UAV-acquired data. For that, a total of 232 papers published in international scientific journal databases was examined. We gathered the published material and evaluated their characteristics regarding application, sensor, and technique used. We relate how DL presents promising results and has the potential for processing tasks associated with UAV-based image data. Lastly, we project future perspectives, commentating on prominent DL paths to be explored in the UAV remote sensing field. Our revision consists of a friendly-approach to introduce, commentate, and summarize the state-of-the-art in UAV-based image applications with DNNs algorithms in diverse subfields of remote sensing, grouping it in the environmental, urban, and agricultural contexts.Comment: 38 pages, 10 figure

Counting and Locating High-Density Objects Using Convolutional Neural Network

This paper presents a Convolutional Neural Network (CNN) approach for counting and locating objects in high-density imagery. To the best of our knowledge, this is the first object counting and locating method based on a feature map enhancement and a Multi-Stage Refinement of the confidence map. The proposed method was evaluated in two counting datasets: tree and car. For the tree dataset, our method returned a mean absolute error (MAE) of 2.05, a root-mean-squared error (RMSE) of 2.87 and a coefficient of determination (R$^2$) of 0.986. For the car dataset (CARPK and PUCPR+), our method was superior to state-of-the-art methods. In the these datasets, our approach achieved an MAE of 4.45 and 3.16, an RMSE of 6.18 and 4.39, and an R$^2$ of 0.975 and 0.999, respectively. The proposed method is suitable for dealing with high object-density, returning a state-of-the-art performance for counting and locating objects.Comment: 15 pages, 10 figures, 8 table

Relations among rankings, ROC analysis and calibration applied to machine learning

Aprendizado supervisionado tem sido principalmente utilizado para classificação. Neste trabalho são mostrados os benefícios do uso de rankings ao invés de classificação de exemplos isolados. Um rankeador é um algoritmo que ordena um conjunto de exemplos de tal modo que eles são apresentados do exemplo de maior para o exemplo de menor expectativa de ser positivo. Um ranking é o resultado dessa ordenação. Normalmente, um ranking é obtido pela ordenação do valor de confiança de classificação dado por um classificador. Este trabalho tem como objetivo procurar por novas abordagens para promover o uso de rankings. Desse modo, inicialmente são apresentados as diferenças e semelhanças entre ranking e classificação, bem como um novo algoritmo de ranking que os obtém diretamente sem a necessidade de obter os valores de confiança de classificação, esse algoritmo é denominado de LEXRANK. Uma área de pesquisa bastante importante em rankings é a análise ROC. O estudo de árvores de decisão e análise ROC é bastante sugestivo para o desenvolvimento de uma visualização da construção da árvore em gráficos ROC. Para mostrar passo a passo essa visualização foi desenvolvido uma sistema denominado PROGROC. Ainda do estudo de análise ROC, foi observado que a inclinação (coeficiente angular) dos segmentos que compõem o fecho convexo de curvas ROC é equivalente a razão de verossimilhança que pode ser convertida para probabilidades. Essa conversão é denominada de calibração por fecho convexo de curvas ROC que coincidentemente é equivalente ao algoritmo PAV que implementa regressão isotônica. Esse método de calibração otimiza Brier Score. Ao explorar essa medida foi encontrada uma relação bastante interessante entre Brier Score e curvas ROC. Finalmente, também foram explorados os rankings construídos durante o método de seleção de exemplos do algoritmo de aprendizado semi-supervisionado multi-descrição CO-TRAININGSupervised learning has been used mostly for classification. In this work we show the benefits of a welcome shift in attention from classification to ranking. A ranker is an algorithm that sorts a set of instances from highest to lowest expectation that the instance is positive, and a ranking is the outcome of this sorting. Usually a ranking is obtained by sorting scores given by classifiers. In this work, we are concerned about novel approaches to promote the use of ranking. Therefore, we present the differences and relations between ranking and classification followed by a proposal of a novel ranking algorithm called LEXRANK, whose rankings are derived not from scores, but from a simple ranking of attribute values obtained from the training data. One very important field which uses rankings as its main input is ROC analysis. The study of decision trees and ROC analysis suggested an interesting way to visualize the tree construction in ROC graphs, which has been implemented in a system called PROGROC. Focusing on ROC analysis, we observed that the slope of segments obtained from the ROC convex hull is equivalent to the likelihood ratio, which can be converted into probabilities. Interestingly, this ROC convex hull calibration method is equivalent to Pool Adjacent Violators (PAV). Furthermore, the ROC convex hull calibration method optimizes Brier Score, and the exploration of this measure leads us to find an interesting connection between the Brier Score and ROC Curves. Finally, we also investigate rankings build in the selection method which increments the labelled set of CO-TRAINING, a semi-supervised multi-view learning algorith

The semi-supervised learning algorithm co-training applied to label text documents

Em Aprendizado de Máquina, a abordagem supervisionada normalmente necessita de um número significativo de exemplos de treinamento para a indução de classificadores precisos. Entretanto, a rotulação de dados é freqüentemente realizada manualmente, o que torna esse processo demorado e caro. Por outro lado, exemplos não-rotulados são facilmente obtidos se comparados a exemplos rotulados. Isso é particularmente verdade para tarefas de classificação de textos que envolvem fontes de dados on-line tais como páginas de internet, email e artigos científicos. A classificação de textos tem grande importância dado o grande volume de textos disponível on-line. Aprendizado semi-supervisionado, uma área de pesquisa relativamente nova em Aprendizado de Máquina, representa a junção do aprendizado supervisionado e não-supervisionado, e tem o potencial de reduzir a necessidade de dados rotulados quando somente um pequeno conjunto de exemplos rotulados está disponível. Este trabalho descreve o algoritmo de aprendizado semi-supervisionado co-training, que necessita de duas descrições de cada exemplo. Deve ser observado que as duas descrições necessárias para co-training podem ser facilmente obtidas de documentos textuais por meio de pré-processamento. Neste trabalho, várias extensões do algoritmo co-training foram implementadas. Ainda mais, foi implementado um ambiente computacional para o pré-processamento de textos, denominado PreTexT, com o objetivo de utilizar co-training em problemas de classificação de textos. Os resultados experimentais foram obtidos utilizando três conjuntos de dados. Dois conjuntos de dados estão relacionados com classificação de textos e o outro com classificação de páginas de internet. Os resultados, que variam de excelentes a ruins, mostram que co-training, similarmente a outros algoritmos de aprendizado semi-supervisionado, é afetado de maneira bastante complexa pelos diferentes aspectos na indução dos modelos.In Machine Learning, the supervised approach usually requires a large number of labeled training examples to learn accurately. However, labeling is often manually performed, making this process costly and time-consuming. By contrast, unlabeled examples are often inexpensive and easier to obtain than labeled examples. This is especially true for text classification tasks involving on-line data sources, such as web pages, email and scientific papers. Text classification is of great practical importance today given the massive volume of online text available. Semi-supervised learning, a relatively new area in Machine Learning, represents a blend of supervised and unsupervised learning, and has the potential of reducing the need of expensive labeled data whenever only a small set of labeled examples is available. This work describes the semi-supervised learning algorithm co-training, which requires a partitioned description of each example into two distinct views. It should be observed that the two different views required by co-training can be easily obtained from textual documents through pre-processing. In this works, several extensions of co-training algorithm have been implemented. Furthermore, we have also implemented a computational environment for text pre-processing, called PreTexT, in order to apply the co-training algorithm to text classification problems. Experimental results using co-training on three data sets are described. Two data sets are related to text classification and the other one to web-page classification. Results, which range from excellent to poor, show that co-training, similarly to other semi-supervised learning algorithms, is affected by modelling assumptions in a rather complicated way

Uso da ferramenta PreText para mineração de textos extraídos do NCBI para estudo epistemológico da Informática em Saúde

Este artigo apresenta a utilização da ferramenta PreText como técnica auxiliar a uma pesquisa sobre epistemologia da Informática em Saúde (IS), que visa inferir se a IS se caracteriza como ciência, tecnologia, tecnociência ou arte. O PreText tem por objetivo realizar pré-processamento de textos, transformando-os em um formato estruturado, usando a abordagem bag-of-words, e foi aplicado aos metadados de437.289 resumos de artigos científicos extraídos da base PubMed Central. Os resultados do processamento foram exportados para uma base de dados e relacionados a um coleção de termos de um tesauro especializado em IS construído pelos autores, denominado EpistemIS, e aos metadados dos artigos para geração de estatísticas. Taisrelações possibilitaram compreender a epistemologia da IS, inferindo que esta é uma tecnociência interdisciplinar que atua nos domínios das Ciências da Vida, Ciências da Saúde e Cuidado em Saúde