Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US

The United States spends more than $1B each year on initiatives such as the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed half a decade. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may provide a cheaper and faster alternative. Here, we present a method that determines socioeconomic trends from 50 million images of street scenes, gathered in 200 American cities by Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22M automobiles in total (8% of all automobiles in the US), was used to accurately estimate income, race, education, and voting patterns, with single-precinct resolution. (The average US precinct contains approximately 1000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a 15-minute drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next Presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographic trends may effectively complement labor-intensive approaches, with the potential to detect trends with fine spatial resolution, in close to real time.Comment: 41 pages including supplementary material. Under review at PNA

Methods and Data Sources for Measuring Socio-Economic Factors: A Literature Review

The compiling of the population data, to establish its socioeconomic factors, is a high-cost task for governments and regulatory organizations due to the need for financial and human resources. This limitation makes it almost impossible to count on immediate updated socioeconomic population information. This article compiles a series of alternative data sources and methods that can be applied to reduce the costs and the time required to update such information. The review focus on how these sources and methods have been used in developing countries during time, highlighting the solutions for satisfying the need of updated socioeconomic factors of the population

Detection of curbside storm drain from street level images using Faster R-CNN

Stormwater management is a significant part of modern urban infrastructure. With an increase in climate change due to global warming, this system plays a major role in conserving water and maintaining the environment. Also, they play a significant role in risk management in times of flood. Storm Drains/inlets are essential in modeling this system. Precise mapping of the location of these drains is the key step in improving the infrastructure. State of the art deep learning technique using Faster R-CNN is presented in this thesis to identify the drains on the curb of the road using Google street view images as the primary source. The model is evaluated with 1000 street-level images of streets and highways of Urbana-Champaign, Illinois-USA. The method proposed shows a significant improvement in the detection accuracy of drains by eliminating a significant amount of false positives compared to the previous state of the art machine vision detection techniques. The dataset used for the thesis is available for future researchers

Predictive mapping and its applications for urban planning

If cities of the 21st century are to be, in the words of planners the world over, “just, safe, healthy, accessible, affordable, resilient, and sustainable” (Habitat III), the implementation of planning interventions to these ends requires information about urban spaces and the people who inhabit them. The acquisition of such information depends largely on robust data collection mechanisms, largely absent in many of today’s largest and fastest growing cities. This study investigates the efficacy of predictive mapping, a means of producing demographic data through the interpretation of satellite imagery using machine learning algorithms, as a tool for urban planning through the simulated deployment of the method on five cities in California’s San Joaquin Valley. The study determines the error of predictive mapping models for six different demographic characteristics and suggests that these models tend to produce predictions of relative (as opposed to absolute) accuracy

Sistema multimodal para la evaluación del riesgo de cáncer de mama desde el enfoque de la minería de datos

[ES] A través de la minería de datos, podemos desarrollar sistemas de recomendación que guíen las decisiones de los usuarios. El objetivo del trabajo es el diseño y desarrollo de una aplicación informática que, a partir de la información sobre un determinado paciente, pueda predecir si el tumor de mama en cuestión es benigno o maligno. Más concretamente, la información más relevante que se extraerá del input a clasificar provendrá de descriptores numéricos sobre el tumor, p.ej. radio, textura, área, etc. Este tipo de asistente médico realizará la predicción basándose en métodos supervisados de minería de datos. La entrada de datos del sistema será vía voz o texto escrito en español, tras lo cual se aplicará un preprocesamiento del input con el fin de que el sistema pueda trabajar con datos estructurados. En una siguiente fase, se emplearán métodos como Naïve Bayes, Support Vector Machines (SVM) y aprendizaje profundo con redes neuronales sobre datos de entrenamiento con el fin de que se detecten patrones que permitan la clasificación del input. Este sistema de predicción también será capaz de determinar qué método es más efectivo tras un proceso de autoevaluación. Este sistema se programará en C# dentro del entorno de Microsoft Visual Studio.[EN] Recommendation systems can be developed to guide users¿ decisions through data mining. The aim of this work is to design and develop a computer application that uses the information about a given patient to predict if a specific breast tumor is benign or not. In particular, the most relevant information extracted from the input is provided by numeric descriptors about the tumor, e.g. radius, texture, area, etc. This type of medical assistant is intended to make predictions based on supervised data mining methods. The input of the system, which is via voice or text in Spanish, should be preprocessed to be converted into structured data. In the next step, we apply methods such as Naïve Bayes, Support Vector Machines (SVM) and deep learning with neural networks to training data, so that patterns are discovered to classify the input. This prediction system will also be able to determine the most effective method by means of self-assessment. The application will be developed with C# within the Microsoft Visual Studio environment.Moreno Claver, J. (2019). Sistema multimodal para la evaluación del riesgo de cáncer de mama desde el enfoque de la minería de datos. Universitat Politècnica de València. http://hdl.handle.net/10251/124880TFG

Desenvolvimento de base de dados de imagens, classes e mensuração de úlceras do pé diabético para técnicas de classificação e ferramentas de auxílio a diagnóstico

Dissertação (Mestrado em Engenharia Biomédica)–Programa de pós-graduação em Engenharia Biomédica, Universidade de Brasília, Brasília, 2020.As Úlceras do Pé Diabético (UPDs) estão entre as principais e mais recorrentes complicações do Diabetes Mellitus (DM) na atualidade. Há uma vasta diversidade de tratamentos para as feridas diabéticas, que são consideradas feridas crônicas e de difícil cicatrização. A maior parte dos tratamentos resumem-se em pomadas, coberturas especiais e limpeza semanal realizadas nos ambulatórios. Esses tratamentos demandam longo prazo para a cicatrização de pequenas lesões e nem sempre têm efeito positivo de diminuição da ferida. O tratamento a base de lâminas de látex associadas a fototerapia é uma inovação para cicatrização dessas úlceras em menos tempo e melhor qualidade do tecido regenerado. As feridas diabéticas possuem diferentes estágios, para direcionar o tratamento adequado é de extrema importância a avaliação precisa da lesão. Por este motivo existem diversas escalas de classificação renomadas utilizadas como referência pelos profissionais de saúde. A classificação correta das úlceras é uma dificuldade enfrentada diariamente, pois cada profissional avalia de uma maneira própria. A partir dessa dificuldade dos profissionais, dos pacientes e tendo ciência da relevância de conjuntos de dados robustos, esta pesquisa busca desenvolver uma base de dados sólida com informações de UPDs. Na literatura científica existe carência de dados referentes a úlceras diabéticas, o que dificulta estudos da lesão e automatização de procedimentos repetitivos no ambiente hospitalar. Para que a base de dados atenda aos critérios estabelecidos, um gabarito para medições invasivas foi adaptado e utilizado a cada visita ambulatorial. A mensuração das úlceras diabéticas é um fator que estima a evolução da ferida, é capaz de direcionar o tipo de tratamento adequado e enfatiza o uso de sistemas metrológicos na saúde. O gabarito manuseado neste procedimento recolhe informações da lesão que são de extrema importância, como: Classificação da Universidade do Texas, comprimento, largura, tipo de exsudato, bordas da ferida e outras. Estas avaliações das feridas foram realizadas semanalmente por enfermeiros de dois Ambulatórios de Pé Diabético, que também auxiliaram na mensuração e registros fotográficos. Além das informações de avaliação da lesão organizadas em tabelas, as imagens digitais originais e outra parte segmentada manualmente compõem a base de dados. Este conjunto de informações da lesão possibilitará diversas pesquisas que buscam automatizar a classificação em ambientes de saúde por meio de Aprendizado de Máquina (AM) e estudos de processamento de imagens. O gabarito adaptado foi testado primeiramente em manequim para padronizar o processo nos pacientes. Em seguida os participantes da coleta foram triados nos hospitais e submetidos a seus respectivos tratamentos. Os elementos das feridas nos pacientes do ensaio clínico Rapha® e tratamento SUS foram coletados simultaneamente. Ao fim das coletas realizamos uma análise detalhada da quantidade de informação levantada sobre as feridas, bem como uma análise quantitativa da evolução das medidas ao longo do tratamento. Estas análises permitem observar tanto o progresso proporcionado pelo equipamento Rapha® no que diz respeito à metrologia das feridas, como a avaliação do aspecto de robustez da base de dados construída.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Diabetic Foot Ulcers (DFU) are among the main and most recurring complications of Diabetes Mellitus (DM) today. There is a wide variety of treatments for diabetic wounds, which are considered chronic wounds and difficult to heal. Most treatments are summarized in ointments, special coverings and weekly cleaning performed in outpatient clinics. These treatments require long-term healing of small lesions and do not always have a positive effect on reducing the wound. The treatment based on latex sheets associated with phototherapy is an innovation for healing these ulcers in less time and better quality of regenerated tissue. Diabetic wounds have different stages, in order to target the appropriate treatment it is extremely important to accurately assess the injury. For this reason, there are several renowned classification scales used as a reference by health professionals. The correct classification of ulcers is a difficulty faced daily, as each professional evaluates in his own way. Based on this difficulty of professionals, patients and being aware of the relevance of robust data sets, this research seeks to develop a solid database with information from DFU. There is a lack of data in the scientific literature regarding diabetic ulcers, which makes it difficult to study the injury and automate repetitive procedures in the hospital environment. In order for the database to meet the established criteria, a template for invasive measurements was adapted and used for each outpatient visit. The measurement of diabetic ulcers is a factor that estimates the evolution of the wound, is able to direct the appropriate type of treatment and emphasizes the use of metrological systems in health. The template handled in this procedure collects information about the injury that is extremely important, such as: University of Texas Wound Classification System, length, width, type of exudate, wound edges and others. These wound assessments were carried out weekly by nurses from two Diabetic Foot Clinics, who also helped with measurement and photographic records. In addition to the injury assessment information organized in tables, the original digital images and another manually segmented part make up the database. This set of injury information will enable several researches that seek to automate the classification in healthcare environments through Machine Learning (ML) and image processing studies. The adapted template was first tested on a mannequin to standardize the process in patients. Then, the participants in the collection were screened in hospitals and submitted to their respective treatments. Wound elements in patients in the Rapha® clinical trial and Brazil's Unified Public Health System treatment were collected simultaneously. At the end of the collections, we performed a detailed analysis of the amount of information collected about the wounds, as well as a quantitative analysis of the evolution of the measures along the treatment. These analyzes allow observing both the progress provided by the Rapha® equipment with regard to the metrology of the wounds, as well as the assessment of the robustness aspect of the constructed database