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Declassified images from the Keyhole (KH)-9 HEXAGON mapping camera system (MCS) offer fine-scale details of urban regions. However, these images have seldom been utilized in urban research due to challenges in labelling (collecting training samples), having only a single panchromatic band and classification. To tackle these limitations, this paper focuses on developing a multi-stage reconstructed historical fine-scale urban landscape (RHFUL) pipeline for KH-9 HEXAGON MCS. The proposed pipeline first integrates internalized parameters, hierarchical object-based image analysis properties and class variability to synthesize new features, abbreviated to IHC. Second, the pipeline uses a weak semi-automated supervised labelling (WSSL) approach to acquire training samples. Finally, the training samples and generated features are subjected to the SegNet deep learning architecture. The performance of each step was assessed against corresponding state-of-the-art benchmark approaches for each of synthesizing features, labelling and classification. In the proposed RHFUL pipeline, the proposed IHC provided the most salient information for urban classification, WSSL labelled urban features more accurately, and the SegNet architecture classified more accurately the urban features relative to the benchmarks. Considering the potential advantages, but also limitations of KH-9 HEXAGON MCS images, further research should be undertaken, particularly drawing on the current advances in pattern recognition techniques for contemporary digital satellite sensors. [ABSTRACT FROM AUTHOR]

Ultrahigh pressure (UHP) was employed for the first time as a green method for the synthesis of highly efficient Fe, N co-doped carbon-based (FeNx/C) electrocatalysts for the oxygen reduction reaction (ORR). Compared with traditional pyrolysis under atmospheric conditions, the synthesis of FeNx/C catalysts under UHP could be done efficiently with much less consumption of time, energy and chemicals. The observed highly efficient synthesis and high ORR activity of such catalysts could be due to the fast heating system (12 °C per second) under UHP, which leads to highly efficient doping of heteroatoms on carbon with much less consumption of chemicals and energy; the UHP-induced high graphitization degree of the carbon support and the selective formation of highly active sites of pyridinic N and Fe–Nx for the ORR also contribute in part to the high ORR catalytic activity of the catalyst. The work presented here paves a new way for the green, environmentally friendly synthesis of heteroatom-doped highly efficient catalysts for energy or chemical processes. [ABSTRACT FROM AUTHOR]

Herein, a type of Fe, N-codoped carbon electrocatalyst (FeN x /C, Fe-N-BCNT#BP) containing bamboo carbon nanotubes and displaying bifunctional high catalytic efficiency for both oxygen reduction reaction (ORR) and carbon dioxide reduction reaction (CO2RR) is reported. It shows high electrocatalytic activity and stability for both the ORR process with onset potential of 1.03 V RHE in alkaline and the CO2RR to CO with high faradic efficiency up to 90% and selectivity of about 100% at low overpotential of 0.49 V. For CO2RR to CO, it is revealed that Fe 3 C is active but the activity of FeN x centers is lower than that of C-N-based centers, contrary with that observed for ORR. Due to its low cost and high electrocatalytic performance for these two reduction reactions, the obtained catalyst is very promising for extensive application in future. The revealed huge activity difference of the same types of active sites for different reactions can efficiently guide the synthesis of advanced materials with multifunction., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)