Monitoring Subsidence Area with the Use of Satellite Radar Images and Deep Transfer Learning.

Subsidence, especially in populated areas, is becoming a threat to human life and property. Monitoring and analyzing the effects of subsidence over large areas using in situ measurements is difficult and depends on the size of the subsidence area and its location. It is also time-consuming and costly. A far better solution that has been used in recent years is Differential Interferometry Synthetic Aperture Radar (DInSAR) monitoring. It allows the monitoring of land deformations in large areas with high accuracy and very good spatial and temporal resolution. However, the analysis of SAR images is time-consuming and involves an expert who can easily overlook certain details. Therefore, it is essential, especially in the case of early warning systems, to prepare tools capable of identifying and monitoring subsidence in interferograms. This article presents a study on automated detection and monitoring of subsidence troughs using deep-transfer learning. The area studied is the Upper Silesian Coal Basin (southern Poland). Marked by intensive coal mining, it is particularly prone to subsidence of various types. Additionally, the results of trough detection obtained with the use of convolutional neural networks were compared with the results obtained with the Hough transform and the circlet transform.

Ladder-like Polymer Brushes Containing Conjugated Poly(Propylenedioxythiophene) Chains.

The high stability and conductivity of 3,4-disubstituted polythiophenes such as poly(3,4-ethylenedioxythiophene) (PEDOT) make them attractive candidates for commercial applications. However, next-generation nanoelectronic devices require novel macromolecular strategies for the precise synthesis of advanced polymer structures as well as their arrangement. In this report, we present a synthetic route to make ladder-like polymer brushes with poly(3,4-propylenedioxythiophene) (PProDOT)-conjugated chains. The brushes were prepared via a self-templating surface-initiated technique (ST-SIP) that combines the surface-initiated atom transfer radical polymerization (SI-ATRP) of bifunctional ProDOT-based monomers and subsequent oxidative polymerization of the pendant ProDOT groups in the parent brushes. The brushes prepared in this way were characterized by grazing-angle FTIR, XPS spectroscopy, and AFM. Steady-state and time-resolved photoluminescence measurements were used to extract the information about the structure and effective conjugation length of PProDOT-based chains. Stability tests performed in ambient conditions and under exposure to standardized solar light revealed the remarkable stability of the obtained materials.

Automatic Subsidence Troughs Detection in SAR Interferograms Using Circlet Transform.

This article presents the results of automatic detection of subsidence troughs in synthetic aperture radar (SAR) interferograms. The detection of subsidence troughs is based on the circlet transform, which is able to detect features with circular shapes. Compared to other methods of detecting circles, the circular transform takes into account the finite data frequency. Moreover, the search shape is not limited to a circle but identified on the basis of a certain width. This is especially important in the case of detection of subsidence troughs whose shapes may not be similar to circles or ellipses but to their fragments. The transformation works directly on the image gradient; it does not require further binary segmentation or edge detection as in the case of other methods, e.g., the Hough transform. The entire processing process can be automated to save time and increase reliability compared to traditional methods. The proposed automatic detection method was tested on a differential interferogram that was generated based on Sentinel-1A SAR images of the Upper Silesian Coal Basin area. The test carried out showed that the proposed method is 20% more effective in detecting troughs that than the method using Hough transform.