FCAU-Net for the Semantic Segmentation of Fine-Resolution Remotely Sensed Images

The semantic segmentation of fine-resolution remotely sensed images is an urgent issue in satellite image processing. Solving this problem can help overcome various obstacles in urban planning, land cover classification, and environmental protection, paving the way for scene-level landscape pattern analysis and decision making. Encoder-decoder structures based on attention mechanisms have been frequently used for fine-resolution image segmentation. In this paper, we incorporate a coordinate attention (CA) mechanism, adopt an asymmetric convolution block (ACB), and design a refinement fusion block (RFB), forming a network named the fusion coordinate and asymmetry-based U-Net (FCAU-Net). Furthermore, we propose novel convolutional neural network (CNN) architecture to fully capture long-term dependencies and fine-grained details in fine-resolution remotely sensed imagery. This approach has the following advantages: (1) the CA mechanism embeds position information into a channel attention mechanism to enhance the feature representations produced by the network while effectively capturing position information and channel relationships; (2) the ACB enhances the feature representation ability of the standard convolution layer and captures and refines the feature information in each layer of the encoder; and (3) the RFB effectively integrates low-level spatial information and high-level abstract features to eliminate background noise when extracting feature information, reduces the fitting residuals of the fused features, and improves the ability of the network to capture information flows. Extensive experiments conducted on two public datasets (ZY-3 and DeepGlobe) demonstrate the effectiveness of the FCAU-Net. The proposed FCAU-Net transcends U-Net, Attention U-Net, the pyramid scene parsing network (PSPNet), DeepLab v3+, the multistage attention residual U-Net (MAResU-Net), MACU-Net, and the Transformer U-Net (TransUNet). Specifically, the FCAU-Net achieves a 97.97% (95.05%) pixel accuracy (PA), a 98.53% (91.27%) mean PA (mPA), a 95.17% (85.54%) mean intersection over union (mIoU), and a 96.07% (90.74%) frequency-weighted IoU (FWIoU) on the ZY-3 (DeepGlobe) dataset.

Modeling The Mysterious Tsunami Swirls In The Northeast Coast Of Japan During The March 2011 Tohoku Earthquake

The formation of tsunami swirls near the coast is an obvious oceanographic phenomenon during the occurrence of giant submarine earthquakes and mega-tsunamis. Several tsunami vortices were generated during the Asian tsunami of 2004 and the great Japan tsunami of March 2011 which lasted for several hours.New models of tsunami generation and propagation are hereby proposed and were used to investigate the tsunami inception, propagation and associated formation of swirls in the eastern coast of Japan. The proposed generation model assumes that the tsunami was driven by current oscillations at the seabed induced by the submarine earthquake. The major aim of this study is to develop a tsunami model to simulate the occurrence of tsunami swirls. Specifically, this study attempts to simulate and understand the formation of the mysterious tsunami swirls in the northeast coast of Japan. In addition, this study determines the vulnerability of the Philippines to destructive tsunami waves that originate near Japan. A coarse resolution model was therefore developed in a relatively large area encompassing Japan Sea and the eastern Philippine Sea. On the other hand, a fine-resolution model was implemented in a small area off Sendai coast near the epicenter. The model result was compared with the tsunami record obtained from the National Data Buoy Center with relatively good agreement as far as the height and period of the tsunami are concerned. Furthermore, the fine-resolution model was able to simulate the occurrence of tsunami vortices off Sendai coast with various sizes that lasted for several hours.

Numerical evidence of anomalous energy dissipation in incompressible Euler flows: towards grid-converged results for the inviscid Taylor–Green problem

The well-known energy dissipation anomaly in the inviscid limit, related to velocity singularities according to Onsager, still needs to be demonstrated by numerical experiments. The present work contributes to this topic through high-resolution numerical simulations of the inviscid three-dimensional Taylor–Green vortex problem using a novel high-order discontinuous Galerkin discretisation approach for the incompressible Euler equations. The main methodological ingredient is the use of a discretisation scheme with inbuilt dissipation mechanisms, as opposed to discretely energy-conserving schemes, which – by construction – rule out the occurrence of anomalous dissipation. We investigate effective spatial resolution up to $8192^3$ (defined based on the $2{\rm \pi}$ -periodic box) and make the interesting phenomenological observation that the kinetic energy evolution does not tend towards exact energy conservation for increasing spatial resolution of the numerical scheme, but that the sequence of discrete solutions seemingly converges to a solution with non-zero kinetic energy dissipation rate. Taking the fine-resolution simulation as a reference, we measure grid-convergence with a relative $L^2$ -error of $0.27\,\%$ for the temporal evolution of the kinetic energy and $3.52\,\%$ for the kinetic energy dissipation rate against the dissipative fine-resolution simulation. The present work raises the question of whether such results can be seen as a numerical confirmation of the famous energy dissipation anomaly. Due to the relation between anomalous energy dissipation and the occurrence of singularities for the incompressible Euler equations according to Onsager's conjecture, we elaborate on an indirect approach for the identification of finite-time singularities that relies on energy arguments.

Dual-comb Photothermal Spectroscopy

Dual-comb spectroscopy (DCS) has revolutionized optical spectroscopy by providing broadband spectral measurements with unprecedent resolution and fast response. Photothermal spectroscopy (PTS) offers an ultrasensitive and background-free gas sensing method, which is normally performed using a single-wavelength pump laser. The merging of PTS with DCS may enable a new spectroscopic method by taking advantage of both technologies, which has never been studied yet. Here, we report dual-comb photothermal spectroscopy (DC-PTS) by passing dual combs and a probe laser through a gas-filled anti-resonant hollow-core fiber, where the generated multi-heterodyne modulation of the refractive index is sensitively detected by an in-line interferometer. As an example, we have measured photothermal spectra of acetylene over 1 THz, showing a good agreement with the spectral database. Our proposed DC-PTS provides new opportunities for broadband gas sensing with super-fine resolution and high sensitivity, as well as with a small sample volume and compact configuration.

Extraction of roughness parameters from remotely-sensed products for hydrology applications

Along rivers, where local insitu gauges are unavailable, estimation of river discharge are undirectly derived from the Manning formula that relate discharge to geomorphological characteristics of the rivers and flow conditions. Most components of the Manning formula can currently be derived from spaceborne products except for two features: the unobserved always-wet bathymetry and the roughness coefficient. Global-scale applications use simplified equivalent riverbed shapes and empirical parameters while local-scale applications rely on finer model dynamics, field survey and expert knowledge. Within the framework of the incoming Surface Water and Ocean Topography (SWOT) mission, scheduled for a launch in 2022, and more particularly, the development of the SWOT-based discharge product, fine-resolution but global discharge estimates should be produced. Currently implemented SWOT-based discharge algorithms require prior information on bathymetry and roughness and their performances highly depend on the quality of such priors. Here we introduce an automatic and spaceborne-data-based-only methodology to derive physically-based roughness coefficients to use in one-dimensional hydrological models. The evaluation of those friction coefficients showed that they allow model performances comparable to calibrated models. Finally, we illutrate two cases of application where our roughness coefficients are used as-is to initiate the experiment: a data assimilation experiment designed to correct the roughness parameters and an application around the HiVDI SWOT-based discharge algorithm. In both cases, the roughness coefficients showed promising perspectives by reproducing, for the data assimilation application, and sometimes improving, in the SWOT discharge algorithm case, the calibrated-parameter-based performances.

Generic Framework for Downscaling Statistical Quantities at Fine Time-Scales and Its Perspectives towards Cost-Effective Enrichment of Water Demand Records

The challenging task of generating a synthetic time series at finer temporal scales than the observed data, embeds the reconstruction of a number of essential statistical quantities at the desirable (i.e., lower) scale of interest. This paper introduces a parsimonious and general framework for the downscaling of statistical quantities based solely on available information at coarser time scales. The methodology is based on three key elements: (a) the analysis of statistics’ behaviour across multiple temporal scales; (b) the use of parametric functions to model this behaviour; and (c) the exploitation of extrapolation capabilities of the functions to downscale the associated statistical quantities at finer scales. Herein, we demonstrate the methodology using residential water demand records and focus on the downscaling of the following key quantities: variance, L-variation, L-skewness and probability of zero value (no demand; intermittency), which are typically used to parameterise a stochastic simulation model. Specifically, we downscale the above statistics down to a 1 min scale, assuming two scenarios of initial data resolution, i.e., 5 and 10 min. The evaluation of the methodology on several cases indicates that the four statistics can be well reconstructed. Going one step further, we place the downscaling methodology in a more integrated modelling framework for a cost-effective enhancement of fine-resolution records with synthetic ones, embracing the current limited availability of fine-resolution water demand measurements.