Detection and mapping of burnt areas from time series of MODIS-derived NDVI data in a Mediterranean region

Moderate resolution remote sensing data, as provided by MODIS, can be used to detect and map active or past wildfires from daily records of suitable combinations of reflectance bands. The objective of the present work was to develop and test simple algorithms and variations for automatic or semiautomatic detection of burnt areas from time series data of MODIS biweekly vegetation indices for a Mediterranean region. MODIS-derived NDVI 250m time series data for the Valencia region, East Spain, were subjected to a two-step process for the detection of candidate burnt areas, and the results compared with available fire event records from the Valencia Regional Government. For each pixel and date in the data series, a model was fitted to both the previous and posterior time series data. Combining drops between two consecutive points and 1-year average drops, we used discrepancies or jumps between the pre and post models to identify seed pixels, and then delimitated fire scars for each potential wildfire using an extension algorithm from the seed pixels. The resulting maps of the detected burnt areas showed a very good agreement with the perimeters registered in the database of fire records used as reference. Overall accuracies and indices of agreement were very high, and omission and commission errors were similar or lower than in previous studies that used automatic or semiautomatic fire scar detection based on remote sensing. This supports the effectiveness of the method for detecting and mapping burnt areas in the Mediterranean region.

Software for video-based multi-point frequency measuring and mapping

Complementary programs

Methods and algorithms for video-based multi-point frequency measuring and mapping

Software for video-based multi-point frequency measuring and mapping: http://hdl.handle.net/10045/53429

3D visual sensing of human hand for remote operation of a robotic hand

New low cost sensors and open free libraries for 3D image processing are making important advances in robot vision applications possible, such as three-dimensional object recognition, semantic mapping, navigation and localization of robots, human detection and/or gesture recognition for human-machine interaction. In this paper, a novel method for recognizing and tracking the fingers of a human hand is presented. This method is based on point clouds from range images captured by a RGBD sensor. It works in real time and it does not require visual marks, camera calibration or previous knowledge of the environment. Moreover, it works successfully even when multiple objects appear in the scene or when the ambient light is changed. Furthermore, this method was designed to develop a human interface to control domestic or industrial devices, remotely. In this paper, the method was tested by operating a robotic hand. Firstly, the human hand was recognized and the fingers were detected. Secondly, the movement of the fingers was analysed and mapped to be imitated by a robotic hand.

Combining visual features and Growing Neural Gas networks for robotic 3D SLAM

The use of 3D data in mobile robotics provides valuable information about the robot’s environment. Traditionally, stereo cameras have been used as a low-cost 3D sensor. However, the lack of precision and texture for some surfaces suggests that the use of other 3D sensors could be more suitable. In this work, we examine the use of two sensors: an infrared SR4000 and a Kinect camera. We use a combination of 3D data obtained by these cameras, along with features obtained from 2D images acquired from these cameras, using a Growing Neural Gas (GNG) network applied to the 3D data. The goal is to obtain a robust egomotion technique. The GNG network is used to reduce the camera error. To calculate the egomotion, we test two methods for 3D registration. One is based on an iterative closest points algorithm, and the other employs random sample consensus. Finally, a simultaneous localization and mapping method is applied to the complete sequence to reduce the global error. The error from each sensor and the mapping results from the proposed method are examined.

RGBD Human-Hand recognition for the Interaction with Robot-Hand

New low cost sensors and the new open free libraries for 3D image processing are permitting to achieve important advances for robot vision applications such as tridimensional object recognition, semantic mapping, navigation and localization of robots, human detection and/or gesture recognition for human-machine interaction. In this paper, a method to recognize the human hand and to track the fingers is proposed. This new method is based on point clouds from range images, RGBD. It does not require visual marks, camera calibration, environment knowledge and complex expensive acquisition systems. Furthermore, this method has been implemented to create a human interface in order to move a robot hand. The human hand is recognized and the movement of the fingers is analyzed. Afterwards, it is imitated from a Barret hand, using communication events programmed from ROS.

An improvement of a SLAM RGB-D method with movement prediction derived from a study of visual features

This paper presents a method for the fast calculation of a robot’s egomotion using visual features. The method is part of a complete system for automatic map building and Simultaneous Location and Mapping (SLAM). The method uses optical flow to determine whether the robot has undergone a movement. If so, some visual features that do not satisfy several criteria are deleted, and then egomotion is calculated. Thus, the proposed method improves the efficiency of the whole process because not all the data is processed. We use a state-of-the-art algorithm (TORO) to rectify the map and solve the SLAM problem. Additionally, a study of different visual detectors and descriptors has been conducted to identify which of them are more suitable for the SLAM problem. Finally, a navigation method is described using the map obtained from the SLAM solution.

Comparativa de detectores de características visuales y su aplicación al SLAM

Comunicación presentada en el X Workshop of Physical Agents, Cáceres, 10-11 septiembre 2009.

A study of 2D features for 3D visual SLAM

Paper submitted to the 43rd International Symposium on Robotics (ISR), Taipei, Taiwan, August 29-31, 2012.

Modelling adaptive web applications

Conceptual Modelling approaches for the web need extensions to specify dynamic personalization properties in order to design more powerful web applications. Current approaches provide techniques to support dynamic personalization, usually focused on implementation details. This article presents an extension of the OO-H conceptual modeling approach to address the particulars associated with the design and specification of dynamic personalization. The main benefit is that this specification can be modified without recompile the rest of the application modules. We describe how conventional navigation and presentation diagrams are influenced by personalization properties. In order to model the variable part of the interface logic OO-H has a personalization architecture that leans on a rule engine. Rules are defined based on a User Model and a Reference Model.

Portable 3D laser-camera calibration system with color fusion for SLAM

Nowadays, the use of RGB-D sensors have focused a lot of research in computer vision and robotics. These kinds of sensors, like Kinect, allow to obtain 3D data together with color information. However, their working range is limited to less than 10 meters, making them useless in some robotics applications, like outdoor mapping. In these environments, 3D lasers, working in ranges of 20-80 meters, are better. But 3D lasers do not usually provide color information. A simple 2D camera can be used to provide color information to the point cloud, but a calibration process between camera and laser must be done. In this paper we present a portable calibration system to calibrate any traditional camera with a 3D laser in order to assign color information to the 3D points obtained. Thus, we can use laser precision and simultaneously make use of color information. Unlike other techniques that make use of a three-dimensional body of known dimensions in the calibration process, this system is highly portable because it makes use of small catadioptrics that can be placed in a simple manner in the environment. We use our calibration system in a 3D mapping system, including Simultaneous Location and Mapping (SLAM), in order to get a 3D colored map which can be used in different tasks. We show that an additional problem arises: 2D cameras information is different when lighting conditions change. So when we merge 3D point clouds from two different views, several points in a given neighborhood could have different color information. A new method for color fusion is presented, obtaining correct colored maps. The system will be tested by applying it to 3D reconstruction.

Portable 3D laser-camera calibration system with color fusion for SLAM

Paper submitted to the 43rd International Symposium on Robotics (ISR2012), Taipei, Taiwan, Aug. 29-31, 2012.

Importancia de los procesos de validación topológica en la gestión de alteraciones catastrales

Las tecnologías de la información y la comunicación están consiguiendo que la información geográfica sea asequible a un mayor número de profesionales a través de las Tecnologías de la Información Geográfica. La intervención multidisciplinar en el territorio enriquece la investigación y las formas de aplicación de este tipo de recursos tecnológicos. Pero esta facilidad tecnológica puede suponer el riesgo de un uso inadecuado, por falta de conocimientos técnicos adecuados a la complejidad de la información geográfica o por el mal uso de las aplicaciones informáticas. El trabajo catastral puede beneficiarse mucho del empleo de estas tecnologías de información geográfica, al facilitar el uso, la comunicación y su administración electrónica, pero el desconocimiento de las propiedades geométricas y topológicas de la información geográfica puede llevar a cometer errores de graves consecuencias a profesionales no especializados. En este artículo ofrecemos el resultado de la investigación del trabajo de diversos juristas y técnicos, con el objetivo de desarrollar métodos automatizados y aplicaciones informáticas que permitan a los especialistas no expertos en Cartografía usar este tipo de información con garantías de exactitud al más alto nivel, como una solución eficaz para que la información geográfica con calidad topológica enriquezca la seguridad jurídica en el tráfico inmobiliario.

La Universidad de Alicante Universidad promotora de salud/Universidad Saludable. Un reto y una oportunidad

Justificación: Dentro de la estrategia de Universidades Saludables, la Universidad de Alicante (UA) inicia un proyecto para conocer, difundir y potenciar los activos para la salud. Se plantea dotar de contenido empírico la propuesta de Morgan y Ziglio de usar el modelo de activos para la salud pública identificados por la comunidad universitaria. Objetivos: explorar la factibilidad y los retos de la aplicación de mapeos de activos para la salud en la UA con el fin de que la comunidad universitaria pueda ganar salud, calidad de vida y bienestar. Desarrollo de la experiencia: Formación de promotores de salud: • La promoción de la salud y la teoría salutogénica. • Aproximación al modelo y la estrategia de activos en salud. Enfoque Asset-Based Community Development (ABCD). • Diferencia entre recursos y activos. • Técnicas de observación y diálogo. • Técnicas mixtas: open space, TICs y mapping... • Competencias profesionales, entorno facilitador y apoyo para obtener resultados. Metodología: Lograr el mapeo de los activos en salud, sus entornos y sus estudiantes siguiendo el enfoque de John McKnight. Aplicación de “lo aprendido” en el contexto de la UA: Planificación del proyecto para el año 2014. Resultados: construcción de un mapa de activos para la salud, geolocalizado en la Universidad de Alicante. Dinamización del mapa de activos, estudiando conexiones entre activos y necesidades de la comunidad universitaria con las personas participantes, para realizar propuestas de acción futura. Difusión del mismo a través de tecnologías de la información.

Using a RGB-D camera for 6DoF SLAM

This paper presents a method for fast calculation of the egomotion done by a robot using visual features. The method is part of a complete system for automatic map building and Simultaneous Localization and Mapping (SLAM). The method uses optical flow in order to determine if the robot has done a movement. If so, some visual features which do not accomplish several criteria (like intersection, unicity, etc,) are deleted, and then the egomotion is calculated. We use a state-of-the-art algorithm (TORO) in order to rectify the map and solve the SLAM problem. The proposed method provides better efficiency that other current methods.