Building Footprint Generation Using Improved Generative Adversarial Networks

Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes. In this work, we have proposed improved generative adversarial networks (GANs) for the automatic generation of building footprints from satellite images. We used a conditional GAN with a cost function derived from the Wasserstein distance and added a gradient penalty term. The achieved results indicated that the proposed method can significantly improve the quality of building footprint generation compared to conditional generative adversarial networks, the U-Net, and other networks. In addition, our method nearly removes all hyperparameters tuning.Comment: 5 page

Detection of Karst Features in the Black Hills Area in South Dakota/Wyoming, USA, Based on Evaluations of Remote Sensing Data

Landsat 8, Sentinel 2, Aster, RapidEye and PlanetScope data and Sentinel 1- and Advanced Land Observing Satellite (ALOS)-Phased Array type L-band Synthetic Aperture Radar (PALSAR)-radar images have been evaluated for a karst feature inventory in the Black Hills area in Wyoming/South Dakota, USA. The GeoInformation System (GIS) integrated evaluation of the different satellite data included as well World Imagery files of ESRI and Bing Maps high resolution satellite data of Microsoft. The satellite data revealed several types of circular features related to karst such as enclosed depressions and collapsed dolines as well as traces of tectonic/structural features (visualized by lineament analysis) cutting through youngest sediments, influencing karstification processes. The origin of the circular features is complex and partly unknown, needing further investigations. Digital Elevation Model (DEM) data, such as Aster- and Shuttle Radar Topography Mission (SRTM) DEM data with 30 m and ALOS PASAR DEM with 12.5 m spatial resolution contributed to the detection of depressions, partly related to karst phenomena (sinkholes). Time series of satellite data reveal seasonal changes of the landscape and provide a data base for the documentation of the impact of climate change

Advancing Agricultural Production With Machine Learning Analytics: Yield Determinants for California's Almond Orchards.

Agricultural productivity is subject to various stressors, including abiotic and biotic threats, many of which are exacerbated by a changing climate, thereby affecting long-term sustainability. The productivity of tree crops such as almond orchards, is particularly complex. To understand and mitigate these threats requires a collection of multi-layer large data sets, and advanced analytics is also critical to integrate these highly heterogeneous datasets to generate insights about the key constraints on the yields at tree and field scales. Here we used a machine learning approach to investigate the determinants of almond yield variation in California's almond orchards, based on a unique 10-year dataset of field measurements of light interception and almond yield along with meteorological data. We found that overall the maximum almond yield was highly dependent on light interception, e.g., with each one percent increase in light interception resulting in an increase of 57.9 lbs/acre in the potential yield. Light interception was highest for mature sites with higher long term mean spring incoming solar radiation (SRAD), and lowest for younger orchards when March maximum temperature was lower than 19°C. However, at any given level of light interception, actual yield often falls significantly below full yield potential, driven mostly by tree age, temperature profiles in June and winter, summer mean daily maximum vapor pressure deficit (VPDmax), and SRAD. Utilizing a full random forest model, 82% (±1%) of yield variation could be explained when using a sixfold cross validation, with a RMSE of 480 ± 9 lbs/acre. When excluding light interception from the predictors, overall orchard characteristics (such as age, location, and tree density) and inclusive meteorological variables could still explain 78% of yield variation. The model analysis also showed that warmer winter conditions often limited mature orchards from reaching maximum yield potential and summer VPDmax beyond 40 hPa significantly limited the yield. Our findings through the machine learning approach improved our understanding of the complex interaction between climate, canopy light interception, and almond nut production, and demonstrated a relatively robust predictability of almond yield. This will ultimately benefit data-driven climate adaptation and orchard nutrient management approaches

A Weakly Supervised Approach for Estimating Spatial Density Functions from High-Resolution Satellite Imagery

We propose a neural network component, the regional aggregation layer, that makes it possible to train a pixel-level density estimator using only coarse-grained density aggregates, which reflect the number of objects in an image region. Our approach is simple to use and does not require domain-specific assumptions about the nature of the density function. We evaluate our approach on several synthetic datasets. In addition, we use this approach to learn to estimate high-resolution population and housing density from satellite imagery. In all cases, we find that our approach results in better density estimates than a commonly used baseline. We also show how our housing density estimator can be used to classify buildings as residential or non-residential.Comment: 10 pages, 8 figures. ACM SIGSPATIAL 2018, Seattle, US

Going against the flow: testing the hypothesis of pulsed axial glacier flow

Hypothesised lobe‐like flow of a temperate glacier in southeast Iceland, proposed from an analysis of ice surface crevassing patterns, is appraised from both empirical and theoretical perspectives. The hypothesis comprises the migration of individual lobes (or ‘pulses’) of ice through the glacier body, with central lobes migrating more rapidly along a narrow, central, ‘axial flow corridor’. Our alternative hypothesis is that crevasse patterns at this glacier instead reflect simple surface ice responses to stresses caused by flow over uneven bed topography. To substantiate our rejection of the lobe‐like, pulsed axial flow hypothesis, we provide: (a) evidence for a prominent transverse foliation that exhibits no evidence of shear of the required magnitude to support the hypothesis; and (b) an analysis of ice surface displacement, obtained by feature tracking, that shows a uniform flow field throughout the glacier tongue. We argue that caution needs to be exercised when interpreting glacier flow solely from crevasse patterns and observations of minor displacements along near‐surface fractures and other features