Constructing a knowledge graph of geological hazards literature can facilitate the reuse of geological hazards literature and provide a reference for geological hazard governance. Named entity recognition (NER), as a core technology for constructing a geological hazard knowledge graph, has to face the challenges that named entities in geological hazard literature are diverse in form, ambiguous in semantics, and uncertain in context. This can introduce difficulties in designing practical features during the NER classification. To address the above problem, this paper proposes a deep learning-based NER model; namely, the deep, multi-branch BiGRU-CRF model, which combines a multi-branch bidirectional gated recurrent unit (BiGRU) layer and a conditional random field (CRF) model. In an end-to-end and supervised process, the proposed model automatically learns and transforms features by a multi-branch bidirectional GRU layer and enhances the output with a CRF layer. Besides the deep, multi-branch BiGRU-CRF model, we also proposed a pattern-based corpus construction method to construct the corpus needed for the deep, multi-branch BiGRU-CRF model. Experimental results indicated the proposed deep, multi-branch BiGRU-CRF model outperformed state-of-the-art models. The proposed deep, multi-branch BiGRU-CRF model constructed a large-scale geological hazard literature knowledge graph containing 34,457 entities nodes and 84,561 relations.

Geological remote sensing interpretation can extract elements of interest from multiple types of images, which is vital in geological survey and mapping, especially in inaccessible regions. However, due to numerous classes, high interclass similarities, complex distributions, and sample imbalances of geological elements, the interpretation results of machine learning (ML)-based methods are understandably worse than manual visual interpretation. In addition, scholars in remote sensing have mainly carried out their works to interpret a single geological element category, such as mineral, lithological, soil, and structure. The interpretation of multiple geological elements is missing, which is more in line with the open world. To improve the interpretation results of ML-based methods and reduce the labor cost in geological survey and mapping, we propose a deep learning (DL)-feature-based adaptive multisource data fusion network (AMSDFNet) for the efficient interpretation of multiple geological remote sensing elements. The AMSDFNet has two branches for learning valuable spatial and spectral information from two kinds of data sources, in which the atrous spatial pyramid pooling (ASPP) operation and an attention block are applied to adaptively extract and fuse multiscale informative features. A hard example mining algorithm was also added to select important training examples to address sample imbalance. A large-scale region in western China with sufficient geological elements was set as the research area. The proposed model improved the two critical metrics by about 2% in the experiment section. As far as we know, this research work is the first time DL features and multisource remote sensing images have been utilized to simultaneously interpret geological elements of lithology, soil, surface water, and glaciers. The extensive experimental results demonstrated the superiority of DL features and our model in geological remote sensing interpretation.

Sub-meter high-resolution remote sensing (HRRS) image land cover classification could provide significant help for urban monitoring, management, and planning. Deep learning-based models have achieved remarkable performance in many land cover classification tasks through end-to-end supervised learning. However, the excellent performance of deep learning-based models relies heavily on a large number of well-annotated samples, which is impossible in practical land cover classification scenarios. Additionally, the training set could contain all of the different land cover types. To overcome these problems, in this paper a semi-supervised multiple-CNN ensemble learning method, namely the Semi-MCNN, is proposed to solve the land cover classification problem in Shenzhen city. Considering the lack of labelled samples, a semi-supervised learning strategy was adopted to leverage large amounts of unlabelled data. In the proposed approach, an automatic sample selection strategy called an ensembled teacher model dataset generation (EMDG) was adopted to select samples and generate a dataset from large amounts of unlabelled data automatically. To tackle the error-propagation problem, an important strategy was adopted to correct the errors by pretraining on the selected unlabelled data and finetuning on the labelled data. Moreover, the semi-supervised strategy together with the multi-CNN ensemble strategy were integrated into an end-to-end architecture. This could significantly improve the generalisation ability of the semi-supervised model, as well as improve the classification accuracy. Experiments were conducted on Shenzhen's land cover data (ShenzhenLC) and two other public remote sensing datasets. These experiments confirmed the superior performance of the proposed semi-MCNN compared to the state-of-the-art land cover classification models.

Urban informal settlements (UISs) are high-density population settlements with low standards of living and supply. UIS semantic segmentation, which identifies pixels corresponding to informal settlements in remote sensing images, is crucial to the estimation of poor communities, urban management, resource allocation, and future planning, particularly in megacities. However, most studies on informal settlement mapping are either based on parcels (image classification) or pixels (semantic segmentation). Few studies utilize object information to improve UIS mapping. Since informal settlements are formed by buildings (objects), utilizing object information can improve UIS semantic segmentation. Furthermore, current UIS mapping studies mainly focus on using single-modality remote sensing images, and there is a lack of related research on using multimodal data. Due to the spatial heterogeneity of informal settlements, using only a single modality of remote sensing image features limits the effectiveness and accuracy of informal settlements semantic segmentation. Aiming at achieving fine-scale UIS mapping results, this article proposes a UIS semantic segmentation method, namely UisNet, that utilizes a transformer-based block to receive multimodal data, including high-spatial-resolution remote sensing images (parcel- and pixel-level) and building polygon data (object-level) to identify UIS. The experiments were conducted in Shenzhen City, and they confirmed the superior performance of UisNet, which achieved an overall accuracy (OA) of 94.80% and a mean intersection over union (mIoU) of 85.51% in the testing set of the manually labeled UIS semantic segmentation dataset (UIS-Shenzhen dataset) and outperformed the best models on semantic segmentation tasks. Besides, we add a set of experiments on a public dataset [gaofen image dataset (GID) dataset] and compare our method with the current state-of-the-art semantic segmentation methods. Experiments show that the proposed UisNet improves mIoU by 1.64% to 7.58% compared to other methods. This work will be available at https://github.com/RunyuFan/ .

Urban functional zones (UFZs) are the urban spaces divided by various functional activities and are the basic units of daily human activities. UFZ mapping, which identifies the UFZ categories in different spatial areas of a city, is of considerable significance to urban management, design, and sustainable development. Various deep learning-based (DL-based) methods, which achieved remarkable results in an end-to-end supervised process, were proposed for UFZ mapping. However, the excellent performance of DL-based models relies heavily on a large number of well-annotated samples, which is impossible to obtain in practical UFZ mapping scenarios. Obtaining these well-annotated samples requires a lot of manual costs, which greatly limits the outcome of these methods in practical UFZ mapping tasks. In this article, we proposed a UFZ mapping method using OpenStreetMap-based (OSM-based) sample generation and the bi-branch neural network (BibNet). By adopting the idea of OSM-based sample generation, the proposed method utilized large-scale crowdsourcing labeled data (source domain) in OSM to generate a UFZ dataset (target domain) from OSM using remote sensing and social sensing data. Considering the inconsistent response of UFZ to various data observations, it is difficult to fully reflect the characteristics of UFZs using only remote sensing or social sensing data. We further proposed the BibNet, which utilizes two different deep neural network branches to comprehensively harness remote sensing images and social sensing data to map the UFZ. Experiments were conducted in Shenzhen City and Hong Kong City (Yau Tsim Mong District, Sham Shui Po District and Kowloon City District). The proposed method achieved an overall accuracy (OA) of 94.46% in the testing set of Shenzhen City and OA of 91.90% in the testing set of Hong Kong City.