Tile2Vec: Unsupervised representation learning for spatially distributed data

Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distributional hypothesis from natural language -- words appearing in similar contexts tend to have similar meanings -- to spatially distributed data. We demonstrate empirically that Tile2Vec learns semantically meaningful representations on three datasets. Our learned representations significantly improve performance in downstream classification tasks and, similar to word vectors, visual analogies can be obtained via simple arithmetic in the latent space.Comment: 8 pages, 4 figures in main text; 9 pages, 11 figures in appendi

Spatial snow water equivalent estimation for mountainous areas using wireless-sensor networks and remote-sensing products

We developed an approach to estimate snow water equivalent (SWE) through interpolation of spatially representative point measurements using a k-nearest neighbors (k-NN) algorithm and historical spatial SWE data. It accurately reproduced measured SWE, using different data sources for training and evaluation. In the central-Sierra American River basin, we used a k-NN algorithm to interpolate data from continuous snow-depth measurements in 10 sensor clusters by fusing them with 14 years of daily 500-m resolution SWE-reconstruction maps. Accurate SWE estimation over the melt season shows the potential for providing daily, near real-time distributed snowmelt estimates. Further south, in the Merced-Tuolumne basins, we evaluated the potential of k-NN approach to improve real-time SWE estimates. Lacking dense ground-measurement networks, we simulated k-NN interpolation of sensor data using selected pixels of a bi-weekly Lidar-derived snow water equivalent product. k-NN extrapolations underestimate the Lidar-derived SWE, with a maximum bias of −10 cm at elevations below 3000 m and +15 cm above 3000 m. This bias was reduced by using a Gaussian-process regression model to spatially distribute residuals. Using as few as 10 scenes of Lidar-derived SWE from 2014 as training data in the k-NN to estimate the 2016 spatial SWE, both RMSEs and MAEs were reduced from around 20–25 cm to 10–15 cm comparing to using SWE reconstructions as training data. We found that the spatial accuracy of the historical data is more important for learning the spatial distribution of SWE than the number of historical scenes available. Blending continuous spatially representative ground-based sensors with a historical library of SWE reconstructions over the same basin can provide real-time spatial SWE maps that accurately represents Lidar-measured snow depth; and the estimates can be improved by using historical Lidar scans instead of SWE reconstructions

AMPHIBIAN DISTRIBUTION IN THE GEORGIA SEA ISLANDS: IMPLICATIONS FROM THE PAST AND FOR THE FUTURE

We summarized amphibian distributions for 12 coastal islands in Georgia, USA. Occurrence among islands was correlated with life history traits, habitats, island size, distance to other islands, and island geological age. Species’ distributions were determined from published literature. Island sizes and vegetation types were derived from 2011 Georgia Department of Natural Resources habitat maps, which included both federal and state vegetation classification systems. Species occurring on more islands tended to have greater total reproductive output (i.e., life span >4 years, and annual egg production >1,000 eggs) and adults had tolerance of brackish environs. Larger islands had great­er area of freshwater wetlands, predominantly short hydroperiod (<6 months). Species tied to long hydroperiod wetlands (>6 months) were more restricted in their distribution across islands. Overall, larger islands supported more species, but the correlation was weaker for geologically younger Ho­locene islands (age <11,000 years). While Euclidean distance between islands does not necessarily preclude inter-island dispersal, inhospitable habitat for amphibians (brackish tidal marshes and creeks interspersed with wide rivers) suggests that inter-island dispersal is very limited. The paucity of recent occurrence data for amphibians in this dynamic coastal region, let alone standardized annual moni­toring data, hinders efforts to model species’ vulnerability in a region susceptible to sea level rise and development pressure. The most common survey method, standardized amphibian vocal surveys, will detect Anuran reproductive efforts, but is unlikely to ascertain if breeding was successful or to detect salamanders. While it will not replace actual population data, consideration of critical life-history traits and breeding habitat availability can be used to direct management to support long-term species per­sistence in changing environs. Even common amphibians in coastal conservation areas of Georgia are vulnerable to increasing population isolation caused by unsuitable habitat

Re-entrant Layer-by-Layer Etching of GaAs(001)

We report the first observation of re-entrant layer-by-layer etching based on {\it in situ\/} reflection high-energy electron-diffraction measurements. With AsBr$_3$ used to etch GaAs(001), sustained specular-beam intensity oscillations are seen at high substrate temperatures, a decaying intensity with no oscillations at intermediate temperatures, but oscillations reappearing at still lower temperatures. Simulations of an atomistic model for the etching kinetics reproduce the temperature ranges of these three regimes and support an interpretation of the origin of this phenomenon as the site-selectivity of the etching process combined with activation barriers to interlayer adatom migration.Comment: 11 pages, REVTeX 3.0. Physical Review Letters, in press