Predictions of the spatiotemporal distribution of fault events help the management planning and the maintenance scheduling in power transmission systems. To this end, this study proposes an early warning system based on the exploration of cause‐attribute relationships. A comprehensive assessment of all the fault causes is conducted by incorporating entire available environmental attributes in the investigated system as inputs. To cope with the rarely occurred fault causes and environmental elements, a procedure of the weighted association rule mining is established. Then, a risk evaluation model is employed in evaluating the prediction performance and the risks of possible false predictions. Three requirements for the uncertainty are taken into account when this framework is applied in real use: technical reliability, internal reliability, and factitious effectiveness. An empirical study is conducted in a real power transmission system to verify the feasibility of the proposed framework, and the results prove that a flexible and robust prediction can be generated consequently.

The detection of building changes (hereafter ‘building change detection’, BCD) is a critical issue in remote sensing analysis. Accurate BCD faces challenges, such as complex scenes, radiometric differences between bi-temporal images, and a shortage of labelled samples. Traditional supervised deep learning requires abundant labelled data, which is expensive to obtain for BCD. By contrast, there is ample unlabelled remote sensing imagery available. Self-supervised learning (SSL) offers a solution, allowing learning from unlabelled data without explicit labels. Inspired by SSL, we employed the SimSiam algorithm to acquire domain-specific knowledge from remote sensing data. Then, these well-initialised weight parameters were transferred to BCD tasks, achieving optimal accuracy. A novel framework for BCD was developed using self-supervised contrastive pre-training and historical geographic information system (GIS) vector maps (HGVMs). We introduced the improved MS-ResUNet network for the extraction of buildings from new temporal satellite images, incorporating multi-scale pyramid image inputs and multi-layer attention modules. In addition, we pioneered a novel spatial analysis rule for detecting changes in building vectors in bi-temporal images. This rule enabled automatic BCD by harnessing domain knowledge from HGVMs and building upon the spatial analysis of building vectors in bi-temporal images. We applied this method to two extensive datasets in Liuzhou, China, to assess its effectiveness in both urban and suburban areas. The experimental results demonstrated that our proposed approach offers a competitive quantitative and qualitative performance, surpassing existing state-of-the-art methods. Combining HGVMs and high-resolution remote sensing imagery from the corresponding years is useful for building updates.