Characterization of the Abydos region through OSIRIS high-resolution images in support of CIVA measurements

Context. On 12 November 2014, the European mission Rosetta delivered the Philae lander on the nucleus of comet 67P /Churyumov-Gerasimenko (67P). After the first touchdown, the lander bounced three times before finally landing at a site named Abydos. Aims. We provide a morphologically detailed analysis of the Abydos landing site to support Philae's measurements and to give context for the interpretation of the images coming from the Comet Infrared and Visible Analyser (CIVA) camera system onboard the lander. Methods. We used images acquired by the OSIRIS Narrow Angle Camera (NAC) on 6 December 2014 to perform the analysis of the Abydos landing site, which provided the geomorphological map, the gravitational slope map, the size-frequency distribution of the boulders. We also computed the albedo and spectral reddening maps. Results. The morphological analysis of the region could suggest that Philae is located on a primordial terrain. The Abydos site is surrounded by two layered and fractured outcrops and presents a 0.02 km(2) talus deposit rich in boulders. The boulder size frequency distribution gives a cumulative power-law index of 4.0 + 0.3/0.4, which is correlated with gravitational events triggered by sublimation and /or thermal fracturing causing regressive erosion. The average value of the albedo is 5.8% at lambda(1) = 480.7 nm and 7.4% at lambda(2) = 649.2 nm, which is similar to the global albedos derived by OSIRIS and CIVA, respectively.

Animation with all MODIS fire product maps from 2000 to 2011, Yakutia, Siberia

The video FireMovie_2000-2011.avi shows an animation with all MODIS fire product maps of the area sequenced over time. Colors in the video describe MODIS classes as follows: MODIS classification and color scale: Class 0 - not processed - Dark blue (1 frame) Class 3 - water - Light Blue (rivers and some lakes) Class 4 - clouds - Green blue Class 5 - non fire land - Yellow green Class 8 - nominal confidence fire - Red Class 9 - high confidence fire - Dark red

A combined measure for quantifying and qualifying the topology preservation of growing self-organizing maps

The Self-OrganizingMap (SOM) is a neural network model that performs an ordered projection of a high dimensional input space in a low-dimensional topological structure. The process in which such mapping is formed is defined by the SOM algorithm, which is a competitive, unsupervised and nonparametric method, since it does not make any assumption about the input data distribution. The feature maps provided by this algorithm have been successfully applied for vector quantization, clustering and high dimensional data visualization processes. However, the initialization of the network topology and the selection of the SOM training parameters are two difficult tasks caused by the unknown distribution of the input signals. A misconfiguration of these parameters can generate a feature map of low-quality, so it is necessary to have some measure of the degree of adaptation of the SOM network to the input data model. The topologypreservation is the most common concept used to implement this measure. Several qualitative and quantitative methods have been proposed for measuring the degree of SOM topologypreservation, particularly using Kohonen's model. In this work, two methods for measuring the topologypreservation of the Growing Cell Structures (GCSs) model are proposed: the topographic function and the topology preserving map