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With the rapid development of geospatial science, artificial intelligence, and high-performance computing, geospatial artificial intelligence has become the major technique for processing and analyzing geospatial big data, and will be widely used in the scientific research and engineering application of earth science, spatial cognition, and smart city. Geospatial intelligence, as an inter-discipline between geospatial science and artificial intelligence, is driven by dual disciplines. At present, it has made important progress in hardware research, system development, data and model sharing, services and applications, and is also facing new challenges. This paper first describes the conceptual evolution of geospatial artificial intelligence and lists the frameworks of some technical systems. The paper then reviews the state-of-the-art in the scientific research and typical application domains. The problems and challenges facing geospatial intelligence are analyzed. Finally, some future prospects and trends are presented.

Cropland is a scarce land resource in hilly and mountainous areas, which has the characteristics of complex topographic conditions and diverse planting structures, leading to the difficulty of rapid and accurate acquisition of cropland information in mountainous areas. Therefore, it is difficult to extract the cropland information in mountainous areas quickly and automatically based on the traditional remote sensing data and remote sensing monitoring methods. Aiming at this problem, this paper takes Xifeng County of Guizhou Province in the southwest mountainous area as the experimental area. According to the heterogeneity of geospatial space, this paper proposes the idea of cropland morphological information extraction by geographical division control and stratification extraction, and constructs a method for extracting cropland morphological information based on geographical division control and stratification extraction under the constraints of geomorphic unit. Firstly, according to the geomorphology-vegetation characteristics, the experimental area is divided into three geographical zones: flatland, hillside area and forest. Then, on the basis of each type of partition, the cropland is divided into different types according to the visual characteristics presented by the cropland, and different deep learning models are designed for hierarchical extraction of different types of cropland. The experimental results show that this method has a good suppression effect on the background noise of complex terrain in mountainous areas, and the extracted cropland plot information is more consistent with the actual distribution pattern of the actual cropland compared with the traditional method, which effectively reduces the rate of missing extraction and wrong extraction.

Accurately identifying roadside objects like trees, cars, and traffic poles from mobile LiDAR point clouds is of great significance for some applications such as intelligent traffic systems, navigation and location services, autonomous driving, and high precision map. In the paper, we proposed a point-group-view network (PGVNet) to classify the roadside objects into trees, cars, traffic poles, and others, which utilize and fuse the advanced global features of multi-view images and the spatial geometry information of point cloud. To reduce redundant information between similar views and highlight salient view features, the PGVNet model employs a hierarchical view-group-shape architecture to split all views into different groups according to their discriminative level, which uses the pre-trained VGG network as the bone network. In view-group-shape architecture, global-level significant features are further generated from group descriptors with their weights. Moreover, an attention-guided fusion network is used to fuse the global features from multi-view images and local geometric features from point clouds. In particular, the global advanced features from multi-view images are quantified and leveraged as the attention mask to further refine the intrinsic correlation and discriminability of the local geometric features from point clouds, which contributions to recognize the roadside objects. We have evaluated the proposed method on five different mobile LiDAR point cloud data. Five test datasets of different urban scenes by different mobile laser scanning systems are used to evaluate the validities of the proposed method. Four accuracy evaluation metrics precision, recall, quality and Fscore of trees, cars and traffic poles on the selected testing datasets achieve (99.19%, 94.27%, 93.58%, 96.63%), (94.20%, 97.56%, 92.02%, 95.68%), (91.48%, 98.61%, 90.39%, 94.87%), respectively. Experimental results and comparisons with state-of-the-art methods demonstrate that the PGVNet model is available to effectively identify roadside objects from the mobile LiDAR point cloud, which can provide data support for elements construction and vectorization in high precision map applications.

Simultaneous localization and mapping (SLAM) has broad application prospects in many fields due to its high energy efficiency and low power consumption. However, there are still some problems in the traditional SLAM system: the key frame in the traditional visual odometer does not contain semantic information, the image information obtained by the mobile robot is relatively single, and the key frame in the actual scene always contains a large number of mismatched points and dynamic points. In response to the above problems, this paper proposes a new idea of semantic SLAM mapping technology. First, in order to find the correct and corresponding feature points, abandon the interference of dynamic points and mismatched points simultaneously, a method for judging the feature state of adjacent frames based on the Lucas-Kanade optical flow method is proposed, and this function is regarded as a new feature. The thread is added to the visual odometry part of ORB-SLAM3 to complete the optimization and improvement of part of the traditional SLAM framework. Secondly, in view of the problem that the image frame obtained by the front-end visual odometer of the traditional SLAM system does not contain any semantic information, the target detection algorithm based on YOLOV4 and the Mask R-CNN semantic segmentation algorithm fused with fully connected conditional random field CRF are used to compare ORB-SLAM3. The key frame image processing of the robot effectively improves the perception of the indoor environment of smart devices such as robots.

The rapid development of earth observation projects in China has obtained a large volume of multi-source (multi-type sensors, multi-temporal, multi-scale) remote sensing data. But the capability of intelligent remote sensing image processing lags behind data acquisition. In recent years, people have significantly improved the effectiveness of image feature extraction with deep learning networks. But limited number and variety of sample data is not enough for processing the multi-source remote sensing images. This paper analyzed existing sample datasets and proposed a method for constructing a sample database for intelligent remote sensing image interpretation, including the data model, classification system, data organization, as well as the Internet-based platform for collaborative sample collection and sharing.

Accurate interpretation of remote sensing image (RSI) plays a vital role in the implementation of remote sensing applications. In recent years, deep supervised learning has achieved great success in the field of RSI interpretation by its soaring performance on representation learning. However, this method heavily relies on large-scale and high-quality labeled data, while building a big remote sensing data set is extremely expensive because of the unique spatial-temporal heterogeneity of remote sensing data. This contradiction seriously restricts the performance of deep supervised learning in large areas and complicated remote sensing scenes. How to solve the last mile problem in the field of RSI accurate interpretation becomes urgent. This paper first systematically reviews the main research progress of supervised learning methods in the field of RSI interpretation, and then analyzes its limitations. Afterward, we introduce the concept of self-supervised learning and detail how it works for unsupervised feature learning. Finally, we briefly discuss open problems and future directions of self-supervised learning if it is applied in the field of RSI interpretation, with the aim of providing a new perspective for RSI interpretation with the adoption of huge unlabeled data.

In recent years, point cloud has become an important type of 3D spatial data. How to improve the understanding abilities of point cloud using artificial intelligence for correct semantic labeling and accurate detection of objects is an urgent and difficult problem. This paper hence proposes an end-to-end 3D point cloud deep learning network, which effectively guarantees the efficiencies of point cloud sampling, the accuracy of feature extraction and the optimization of the overall network performance by the up-down sampling strategy of irregular distribution point cloud, multi-layer aggregation and propagation of features and the loss function for uneven samples. The studies on the large-scale 3D point cloud benchmark data show that it achieves excellent performance in semantic labeling for large-scale outdoor scenes of point clouds, better than those of the state-of-art deep learning networks of point cloud, providing a strong support for the high-performance extraction of 3D geospatial information.

Surface subsidence not only affects the sustainable development of social economy, but also threatens the safety of human life. High precision prediction of surface subsidence is of great significance for the prevention of geological disasters. However, the existing prediction methods are difficult to obtain reliable prediction results because of the model parameters or the lack of relevant data. For this problem, a method of surface subsidence prediction based on deep learning is proposed. Firstly, the multiple master-image coherent target small-baseline InSAR (MCTSB-InSAR) is used to obtain the inversion results of large area and high precision ground deformation time series. Secondly, the cyclic neural network is used as the network framework, and the long short-term memory (LSTM) model is used to learn the characteristics of ground settlement. Finally, the grid search method is used to adjust the model parameters, then get the optimal combination scheme of model parameters. The actual observation results show that the average absolute error (0.3 mm) of the prediction model proposed in this paper is reduced by 27.3% at least, and the average prediction accuracy of differential settlement is improved by 8.9% at least. The results of spatial pattern analysis show that the LSTM model is effective for the short-term prediction of large-scale time series deformation.

Influenced by the performance of imaging spectrometer and the distribution of complex ground objects, hyperspectral images have a large number of mixed pixels. Traditional learning-based unmixing methods are shallow models, or lack of comprehensive use of spatial and spectral information. This paper proposes a collaborative deep model with multi-dimensional convolutional network. Using multi-dimensional convolutional network can make full use of multi-dimensional semantic information, which is better to estimate hyperspectral mixed pixel abundance with small samples. The method augments training data, constructs three kinds of convolutional neural networks: spectral dimension, spatial dimension and cube dimension; the method designs fusion layer to concatenate features with three kinds of convolutional neural networks, and to “end-to-end” estimate of mixed pixel abundance; the model uses batch normalization, pooling and dropout to avoid over fitting phenomenon. The experimental results indicate that the introduction of our proposed method can extract spatial-spectral feature information more effectively. Compared with other convolutional network unmixing models, the accuracy of the estimated mixed pixel abundance is significantly improved.