Analyzing Multiscale Spatial Relationships between the House Price and Visual Environment Factors

House price is closely associated with the development of the national economy and people’s daily life. Understanding the spatial distribution characteristics and influencing factors of the house price is of great practical significance. Although a lot of attention has been paid to modeling the house price from structure and location attributes, limited work has considered the impact of visual attributes. Intuitively, a better visual environment may raise the surrounding house price. When aggregating multiple factors that influence house price, the multiscale geographically weighted regression (MGWR) provides a suitable solution. Specifically, the MGWR assigns each factor a bandwidth to model the spatial heterogeneity, e.g., a factor may have different influences at different places. In this paper, we introduce the visual environment factors into the MGWR method. In detail, we extract ten visual elements, e.g., sky, vegetation, road, from the Baidu street view (BSV) images, using a deep learning framework. We further define six visual environment factors to investigate their influence on house price. Based on the data from two representative Chinese cities, i.e., Beijing and Chongqing, we reveal the influence degree and spatial scale difference of six visual indexes on the house price in two cities. Results show that: (1) the influence intensity of our proposed six visual environment factors on the house price in different regions of the city can be identified, and the green view index (GVI) is the most important visual environmental factor; and (2) the influence of these view indexes changes significantly or even reversely depends on different areas.

X-Ray Image Recognition Based on Improved Mask R-CNN Algorithm

It is an important part of security inspection to carry out security and safety screening with X-ray scanners. Computer vision plays an important role in detection, recognition, and location analysis in intelligent manufacturing. The object detection algorithm is an important part of the intelligent X-ray machine. Existing threat object detection algorithms in X-ray images have low detection precision and are prone to missed and false detection. In order to increase the precision, a new improved Mask R-CNN algorithm is proposed in this paper. In the feature extraction network, an enhancement path is added to fuse the features of the lower layer into the higher layer, which reduces the loss of feature information. By adding an edge detection module, the training effect of the sample model can be improved without accurate labeling. The distance, overlap rate, and scale difference between objects and region proposals are solved using DIoU to improve the stability of the region proposal’s regression, thus improving the accuracy of object detection; SoftNMS algorithm is used to overcome the problem of missed detection when the objects to be detected overlap each other. The experimental results indicate that the mean Average Precision (mAP) of the improved algorithm is 9.32% higher than that of the Mask R-CNN algorithm, especially for knife and portable batteries, which are small in size, simple in shape, and easy to be mistakenly detected, and the Average Precision (AP) is increased by 13.41% and 15.92%, respectively. The results of the study have important implications for the practical application of threat object detection in X-ray images.

Single Photon Avalanche Diode Arrays for Time-Resolved Raman Spectroscopy

The detection of peaks shifts in Raman spectroscopy enables a fingerprint reconstruction to discriminate among molecules with neither labelling nor sample preparation. Time-resolved Raman spectroscopy is an effective technique to reject the strong fluorescence background that profits from the time scale difference in the two responses: Raman photons are scattered almost instantaneously while fluorescence shows a nanoseconds time constant decay. The combination of short laser pulses with time-gated detectors enables the collection of only those photons synchronous with the pulse, thus rejecting fluorescent ones. This review addresses time-gating issues from the sensor standpoint and identifies single photon avalanche diode (SPAD) arrays as the most suitable single-photon detectors to be rapidly and precisely time-gated without bulky, complex, or expensive setups. At first, we discuss the requirements for ideal Raman SPAD arrays, particularly focusing on the design guidelines for optimized on-chip processing electronics. Then we present some existing SPAD-based architectures, featuring specific operation modes which can be usefully exploited for Raman spectroscopy. Finally, we highlight key aspects for future ultrafast Raman platforms and highly integrated sensors capable of undistorted identification of Raman peaks across many pixels.

Upscaling of outcrop information for improved reservoir modelling – exemplified by a case study on chalk

Outcrops are valuable for analogous subsurface reservoirs in supplying knowledge of fine-scale spatial heterogeneity pattern and stratification types, which are difficult to obtain from subsurface reservoir cores, well logs or seismic data. For petrophysical properties in a domain where the variations are relatively continuous and not dominated by abrupt contrasts, the spatial heterogeneity pattern can be characterized by a semivariogram model. The outcrop information therefore has the potential to constrain the semivariogram for subsurface reservoir modelling, even though it represents different locations and depths, and the petrophysical properties may differ in magnitude or variance. However, the use of outcrop derived spatial correlation information for petrophysical property modelling in practice has been challenged by the scale difference between the small support volume of the property measurements from outcrops and the typically much larger grid cells used in reservoir models. With an example of modelling the porosity of an outcrop chalk unit in eastern Denmark, this paper illustrates how the fine-scale spatial correlation information obtained from sampling of outcrops can be transferred to coarser scale models of analogue rocks. The workflow can be applied to subsurface reservoirs and ultimately improves the representation of geological patterns in reservoir models.

Gear Mapping Technology Based on Differential Envelope Principle

It is proposed that the complex molding process and the scanning process should be unified through the differential envelope principle. After scanning the surface of the part with the contact sensor, the contour coordinates of the measured surface are calculated by the trajectory of the center of the ball, and the contour data of the complex linear surface is extracted. Then, the intelligent analysis and calculation of precision generating surface parameters and quantitative adjustment are carried out, and the exact reverse method of the special line parts with characteristic parameters is studied. There is only micron scale difference between the reverse result and the nominal value. This technology is applied to the closed-loop manufacturing process of gear parts, which reduces the dependence on the artificial experience when the machining parameters of the cylindrical gear processing equipment is adjusted and provides an effective solution for the digital and intelligent manufacturing of the parts.

Effects of grid scale and resulting initial bed disturbance differences on the evolution of braided rivers

<p>Physics-based models have been increasingly developed in recent years and applied to simulate the braiding process and evolution of channel units in braided rivers. Braided rivers are the river network system characterized by the staggered distribution of bars and channels. In the numerical calculation, the grid scale affects the behavior process and morphological description of braided rivers. In this paper, a 2D numerical model is used to simulate the evolution of the braided rivers where the transport of load bed sediment plays a dominant role. In the natural scale braided rivers evolution modeling, the difference of the braided rivers' morphological characteristics under different grid scales is discussed, and the influence of different distribution of topographic disturbance caused by grid scale difference on the morphological characteristics of braided rivers is discussed. The study shows that the grid scale does not affect the description of braided rivers evolution process, and braided rivers evolve in the same way regardless of grid scale (within a reasonable range). However, the statistical characteristics of braided rivers are greatly affected by the grid scale. The braiding index increases as the grid scale decreases, but when the grid scale decreases to a certain extent (20m in this paper), the braiding index no longer increases. The number and average height of bars in braided rivers increase with the decrease of grid scale, and the average area of bar near riverbed also increases with the decrease of grid scale, but the average area of bar near water surface does not change with the change of grid scale. In general, the higher the grid resolution is, the more similar the bar morphology in the numerical model is to that in natural rivers. In addition, the different distribution of topographic disturbance caused by the grid scale difference has an influence on the braiding intensity and the bar morphology of the braided rivers, but the influence degree is much smaller than that caused by the grid scale difference.</p>

Using a global tree sap flow database as ground-truth for transpiration products validation

<p>Transpiration (T) is a key driver of ecosystem energy, water and carbon flows and is tightly linked to climate and land-use change. While global models rely extensively on remotely sensed transpiration products to evaluate land-surface processes, ground-truth validation for these products does not exist. At best, data from eddy-covariance evapotranspiration is used, but the T component is partitioned based-on a set of complex assumptions, which are in themselves poorly validated for many parts of the world. Sapflow (SF) measurements allow direct quantification of tree-level T which can be used as ground-truth for T-products in forested areas. A recent global network of sapflux data, (SapFluxNet – SFN) has provided the first quality-controlled sapflow dataset at a global scale, opening up new opportunities to evaluate global T products.  Using the SFN-SF and Global Land Evaporation Amsterdam Model (GLEAM) T product, we address i) how the time course of the two products scale with one another, and ii) whether this scaling is different between days with low, median or high T/ SF within months; in addition, iii) we evaluate errors patterns of GLEAM-T in relation to SFN-SF and test whether these errors are biased by site climate or by model inputs. Our results shows GLEAM-T scales with SFN-SF, especially for days with median transpiration, but this scaling, rather than 1:1, has a slope of 0.9, which causes underestimation of SFN-SF at high GLEAM-T values. The scaling is shallower for low and high transpiration days leading to a higher bias in those days. In addition, GLEAM-T scales from SFN-SF with an offset, which compensate the shallower scaling at median values at the expense of increasing bias at extremes. Our results also show errors of GLEAM-T in relation to SFN-SF are not random but depend on the location`s climate and on the soil moisture stress factor used within GLEAM transpiration model. Our work bridges, for the first time, the scale difference between trees and pixels and shows the potential of using ground-truth SF measurements for evaluating biases and patterns in global products.</p>

The Indian COSMOS Network (ICON): Validating L-Band Remote Sensing and Modelled Soil Moisture Data Products

Availability of global satellite based Soil Moisture (SM) data has promoted the emergence of many applications in climate studies, agricultural water resource management and hydrology. In this context, validation of the global data set is of substance. Remote sensing measurements which are representative of an area covering 100 m2 to tens of km2 rarely match with in situ SM measurements at point scale due to scale difference. In this paper we present the new Indian Cosmic Ray Network (ICON) and compare it’s data with remotely sensed SM at different depths. ICON is the first network in India of the kind. It is operational since 2016 and consist of seven sites equipped with the COSMOS instrument. This instrument is based on the Cosmic Ray Neutron Probe (CRNP) technique which uses non-invasive neutron counts as a measure of soil moisture. It provides in situ measurements over an area with a radius of 150–250 m. This intermediate scale soil moisture is of interest for the validation of satellite SM. We compare the COSMOS derived soil moisture to surface soil moisture (SSM) and root zone soil moisture (RZSM) derived from SMOS, SMAP and GLDAS_Noah. The comparison with surface soil moisture products yield that the SMAP_L4_SSM showed best performance over all the sites with correlation (R) values ranging from 0.76 to 0.90. RZSM on the other hand from all products showed lesser performances. RZSM for GLDAS and SMAP_L4 products show that the results are better for the top layer R = 0.75 to 0.89 and 0.75 to 0.90 respectively than the deeper layers R = 0.26 to 0.92 and 0.6 to 0.8 respectively in all sites in India. The ICON network will be a useful tool for the calibration and validation activities for future SM missions like the NASA-ISRO Synthetic Aperture Radar (NISAR).

Alligator presence influences colony site selection of long-legged wading birds through large scale facilitative nest protector relationship

Positive ecological relationships, such as facilitation, are an important force in community organization. The effects of facilitative relationships can be strong enough to cause changes in the distributions of species and in many cases have evolved as a response to predation pressure, however, very little is known about this potential trend in vertebrate facilitative relationships. Predation is an important selective pressure that may strongly influence breeding site selection by nesting birds. The American Alligator (Alligator mississippiensis) facilitates a safer nesting location for wading birds (Ciconiiformes and Pelecaniformes) by deterring mammalian nest predators from breeding sites. However, alligators do not occur throughout the breeding range of most wading birds, and it is unclear whether alligator presence affects colony site selection. We predicted that nesting wading birds change colony site preferences when alligators are not present to serve as nest protectors. Within the northern fringe of alligator distribution we compared colony characteristics in locations where alligator presence was either likely or unlikely while controlling for availability of habitat. Wading birds preferred islands that were farther from the mainland and farther from landmasses > 5 ha when alligator presence was unlikely compared to when alligators were likely. These findings indicate that wading birds are seeking nesting locations that are less accessible to mammalian predators when alligators are not present, and that this requirement is relaxed when alligators are present. This study illustrates how a landscape-scale difference between realized and fundamental niche can result from a facilitative relationship in vertebrates.