BUSCADOR RECOLECTA

Description of the project

Description of the dataset

Methodology

• Step 1. Downloading the Quick, Draw! images in binary format.
• Step 2. Selection of 1200 images for each of the 345 classes (414000 elements in total).
• Step 3. The image size is 256x256 and they are converted from vector format into pixel format.
• Step 4. The 414000 images are divided into three sets: training (289800 elements), validation (62100 elements) and test (62100 elements).

Descripción del proyecto

Descripción del dataset

Metolodogía

• Etapa 1. Descarga de las imágenes de Quick, Draw! en formato binario.
• Etapa 2. Selección de 1200 imágenes por cada una de las 345 clases (414000 elementos en total).
• Etapa 3. El tamaño de las imágenes es de 256x256 y son transformadas de formato vectorial a formato de píxel.
• Etapa 4. Las 414000 imágenes son divididas en tres conjuntos: entrenamiento (289800 elementos), validación (62100 elementos) y test (62100 elementos).

Cellular evolutionary algorithms (cEAs) use structured populations whose evolutionary cycle is governed by local interactions among individuals. This helps to prevent the premature convergence to local optima that usually takes place in panmictic populations. The present work extends cEAs by means of a message passing phase whose main effect is a more effective exploration of the search space. The mutated offspring that potentially replaces the original individual under cEAs is considered under message passing cellular evolutionary algorithms (MPcEAs) as a message sent from the original individual to itself. In MPcEAs, unlike in cEAs, a new message is sent from the original individual to each of its neighbors, representing a neighbor’s mutated offspring whose second parent is selected from the neighborhood of the original individual. Thus, every individual in the population ultimately receives one additional candidate for replacement from each of its neighbors rather than having a unique candidate. Experimental tests conducted in the domain of real function optimization for continuous search spaces show that, in general, MPcEAs significantly outperform cEAs in terms of effectiveness. Specifically, the best solution obtained through MPcEAs has an importantly improved fitness quality in comparison to that obtained by cEAs.

The reference dataset RP_LAC_2019_S2 was obtained from S2 images over a set of 56 mosaics (sampling units) sampled following a custom design for the Sentinel mosaic grid system and to represent both major fire regimes and regimes with lower burned area of Latin America and Caribbean in the different ecoregions. For each mosaic, S2 time series were defined based on a set of conditions to minimize cloudiness and ensure series length and minimum time lag between image pairs. The S2 image pairs were classified with a Random Forest (RF) algorithm to provide burned perimeters representing burned areas between the two dates that were combined into a synthetic burned area reference dataset. This dataset represents for each unit burned and unburned polygons and masked areas. The RP_LAC_2019_S2 dataset is part of the Burned Area Reference Database (BARD), a database that compiles multitemporal global and regional burned area reference datasets for Earth Observation burned area products validation.

Description of the project: This dataset has been developed for the validation of the S2BA (Sentinel-2 Burned Area) product in Latin America and Caribbean for the year 2019. S2BA is an automatic global burned area mapping algorithm (S2BA) based on Sentinel-2 Level-2A imagery in combination with Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectrometer (MODIS) active fire data. The algorithm and product was developed by the University of the Basque Country UPV/EHU under the "Proyecto Estratégico Análisis y explotación de información geoespacial (GeoInf) PES20/54” Estratégico Análisis y explotación de información geoespacial (GeoInf) PES20/54”

Fractals are a family of shapes formed by irregular and fragmented patterns. They can be classified into two main groups: geometric and algebraic. Whereas the former are characterized by a fixed geometric replacement rule, the latter are defined by a recurrence function in the complex plane. The classical method for visualizing algebraic fractals considers the sequence of complex numbers originated from each point in the complex plane. Thus, each original point is colored depending on whether its generated sequence escapes to infinity. The present work introduces a novel visualization method for algebraic fractals. This method colors each original point by taking into account the complex number with minimum modulus within its generated sequence. The advantages of the novel method are twofold: on the one hand, it preserves the fractal view that the classical method offers of the escape set boundary and, on the other hand, it additionally provides interesting visual details of the prisoner set (the complement of the escape set). The novel method is comparatively evaluated with other classical and non-classical visualization methods of fractals, giving rise to aesthetic views of prisoner sets.