Address matching is the key to improve the efficiency of location-based services (LBS). Most current methods mainly focus on encoding features from address texts and have not fully explored the geospatial information of address. Although there are methods that incorporate longitude and latitude, they neglect the absolute coordinate information and neighborhood distribution characteristics within them. The insufficient encoding of geographic information seriously affects the accuracy of address text matching. Therefore, this paper proposes a Multi-Modal Geospatial Encoding Network (MGENet) for Chinese address matching. Specifically, the MGENet uses Geohash encoding to model the spatial information of longitude and latitude, and constructs spatial features to express the neighborhood distribution of address entities. Additionally, a multi-modal semi-interactive layer is incorporated to solve the problem of insufficient interaction between text and spatial features. Experiments on large-scale datasets show that the MGENet can achieve 80.3% in F1 score for Chinese address matching, which is 3.1% higher than that of the state-of-the-art (SOTA) model, Chinese-BERT. Moreover, the integration of semi-interactive layer allows the model to require only 7.48M floating-point operations (FLOPs) during prediction, which greatly promotes the efficiency of downstream services.

The frequent occurrence of nonagriculturalization events has posed significant challenges to global food security and sustainable development. Despite the emerging deep learning (DL) algorithms demonstrating effective capability in capturing changes from remote sensing imagery, they have not consistently maintained favorable performance in cropland semantic change detection (CropSCD) tasks. The primary challenge lies in the natural contradiction between the diverse change classes and the sparse availability of change samples. Furthermore, the scarcity of CropSCD datasets also restricts the capabilities of data-driven models. Therefore, in order to encode diverse semantics from a small amount of change pixels, a memory-guided network (MeGNet) dedicated to CropSCD tasks is proposed. In particular, a class-aware memory module is introduced in the MeGNet to preserve change semantics, which can guide the model to distinguish different change classes. Moreover, a high-resolution CropSCD dataset is also constructed to alleviate the issue of insufficient dataset. The CropSCD dataset comprises 4141 pairs of images, each with a size of$512\times 512$, and is annotated with corresponding labels for eight cropland change classes. Comparative experiments have substantiated the superiority of MeGNet over current state-of-the-art (SOTA) methods, with the highest mean-F1 and mean intersection over union (mIoU) of 71.44% and 58.01% on the high-resolution semantic change detection (HRSCD) dataset, and those of 55.42% and 42.14% on the CropSCD dataset. These results have validated the feasibility and potential of the proposed MeGNet and CropSCD dataset in CropSCD tasks.

In view of the challenges on urban green space (UGS) mapping from high-resolution images (HRIs), including insufficient dataset as well as the intra-class difference and inter-class similarity of UGS in HRIs, we propose a novel network for UGS extraction (UGSNet) and collect an large urban green space dataset (UGSet) with 4,454 samples of size $512\times 512$ in this paper. The UGSNet integrates the attention mechanism to improve the discrimination of UGS, and employs a point head with point rending strategy for precise edge recovery. Comparison experiments with the state-of-the-art (SOTA) semantic segmentation models show that the UGSNet can achieve the highest F1 of 77.30% on UGSet.

Building change detection (BCD) is significant for urban planning and environmental protection. In view of the inter-class similarity and intra-class difference of building changes in complex built-up area, specialized solutions have been introduced in BCD. Mainstream methods include extracting building prior information in advance and enhancing long-range context information. These methods often require additional processing, and ignore the construction of cross-temporal context information, resulting in deficiencies on CD performance and efficiency. Therefore, an end-to-end PA-Former for BCD is proposed in this letter, which combines prior extraction and contextual fusion together by learning prior-aware Transformer. Specifically, the PA-Former adopts a prior-feature extractor to capture prior and deep features from the bi-temporal images, in which a prior interpreter is integrated to obtain priori structural information of buildings. Besides, a prior-aware Transformer module (PATM) is designed to obtain contextual tokens with spatiotemporal information from the prior features, and integrate into the deep features. Extensive experiments with state-of-the-art methods are conducted for comparison. Particularly, the PA-Former surpasses the baselines with an F1 of 88.79% on BCDD dataset and that of 85.32% on Google dataset.

Land cover classification plays a crucial role in land resource monitoring and planning. Recently, deep learning-based methods are becoming the dominating method for precise land cover mapping. However, the large-scale application of them is deeply hindered by the domain shift between different images, which is easily caused by illumination, climate, regional divergence, and so on. With the aim to cope with the problem of domain shift, many domain adaptation (DA) methods have been provided and great achievements have been made, especially the newborn adversarial DA, which usually contains a generator and a discriminator. Among these methods, the pixel-level methods are of high memory consumption, whereas feature-level methods are found hard to decode the structured information for semantic segmentation tasks due to the lack of low-dimensional information. Therefore, we propose an adversarial domain adaptation framework with Kullback–Leibler constraint (KL-ADDA) for remote sensing land cover classification. A state-of-the-art (SOTA) semantic segmentation network is utilized as the generator, which directly outputs the segmentation results to the discriminator to retain more low-level information. Besides, a Kullback–Leibler (KL)-divergence is calculated to improve the discriminative ability of the discriminator and thus enhance the generator’s performance. Experiments on the international society for photogrammetry and remote sensing (ISPRS) data set and two simulated target data sets have shown the effectiveness of KL-ADDA for DA.

Different-resolution change detection (DRCD) is now becoming an urgent problem to be solved, which is of great potential in rapid monitoring, such as disaster assessment, urban expansion, etc. In DRCD tasks, bi-temporal inputs are given in the form of different resolutions, thus conventional CD methods cannot be applied directly. Previous studies have attempted to deal with this problem by reconstructing the low-resolution (LR) image into a high-resolution (HR) one, including interpolation and super-resolution (SR). However, these solutions are limited by the availability of training data, making it hard to meet different kinds of needs. Besides, these image-level strategies have also ignored the interaction and alignment of high-level features. Therefore, we propose a new approach based on multi-model Transformers (MM-Trans), which solves the resolution gaps of bi-temporal inputs in DRCD tasks from the perspective of feature alignment. In the MM-Trans, a weight-unshared feature extractor is first utilized to precisely capture the features of the different-resolution inputs; then a spatial-aligned Transformer (sp-Trans) is introduced to align the LR-image features to the same size of the HR-image ones, which can be optimized in a learnable way by an auxiliary token loss; after that, a semantic-aligned Transformer (se-Trans) is adopted, in which the bi-temporal features can be further interacted and aligned semantically; finally, a prediction head is employed to obtain fine-grained change results. Experiments conducted on three common CD datasets, CDD, S2Looking, and HTCD dataset, have shown the advancement of the MM-Trans and fully demonstrated its potential in DSCD tasks.

Change detection (CD) aims to distinguish surface changes based on bitemporal images. Since high-resolution (HR) images cannot be typically acquired continuously over time, bitemporal images with different resolutions are often adopted for CD in practical applications. Traditional subpixel-based methods for CD using images with different resolutions may lead to substantial error accumulation when the HR images are employed, which is because of intraclass heterogeneity and interclass similarity. Therefore, it is necessary to develop a novel method for CD using images with different resolutions that are more suitable for the HR images. To this end, we propose a super-resolution-based change detection network (SRCDNet) with a stacked attention module (SAM). The SRCDNet employs a super-resolution (SR) module containing a generator and a discriminator to directly learn the SR images through adversarial learning and overcome the resolution difference between the bitemporal images. To enhance the useful information in multiscale features, a SAM consisting of five convolutional block attention modules (CBAMs) is integrated to the feature extractor. The final change map is obtained through a metric learning-based change decision module, wherein a distance map between bitemporal features is calculated. Ablation study and comparative experiments on two large datasets, building change detection dataset (BCDD) and season-varying change detection dataset (CDD), and a real-image experiment on the Google dataset fully demonstrate the superiority of the proposed method. The source code of SRCDNet is available athttps://github.com/liumency/SRCDNet.

Change detection (CD) aims to identify surface changes from bitemporal images. In recent years, deep learning (DL)-based methods have made substantial breakthroughs in the field of CD. However, CD results can be easily affected by external factors, including illumination, noise, and scale, which leads to pseudo-changes and noise in the detection map. To deal with these problems and achieve more accurate results, a deeply supervised (DS) attention metric-based network (DSAMNet) is proposed in this article. A metric module is employed in DSAMNet to learn change maps by means of deep metric learning, in which convolutional block attention modules (CBAM) are integrated to provide more discriminative features. As an auxiliary, a DS module is introduced to enhance the feature extractor’s learning ability and generate more useful features. Moreover, another challenge encountered by data-driven DL algorithms is posed by the limitations in change detection datasets (CDDs). Therefore, we create a CD dataset, Sun Yat-Sen University (SYSU)-CD, for bitemporal image CD, which contains a total of 20 000 aerial image pairs of size$256\times256$. Experiments are conducted on both the CDD and the SYSU-CD dataset. Compared to other state-of-the-art methods, our network achieves the highest accuracy on both datasets, with an F1 of 93.69% on the CDD dataset and 78.18% on the SYSU-CD dataset.

In view of the insufficient of current change detection, we propose a deeply-supervised attention metric-based network (DSAMNet) for bi-temporal image change detection. The DSAMNet contains a CBAM integrated change decision module to learn a change map directly from features from feature extractor, and an auxiliary deep supervision module to generate intermediate change results to help the training of hidden layers. We also provide a new benchmark-SYSU-CD-with totally 20000 image pairs for the training and testing of deep learning based CD methods. Comparative experiments on the SYSU-CD dataset have proved the effectiveness of the proposed method.

Change detection methods based on low-resolution (LR) images with higher temporal resolution often lead to fuzzy results, while high-resolution images (HRIs) can provide more detailed information to solve this problem. However, it's hard to obtain two tiles of HRIs with high-quality for rapid change detection in actual production due to low temporal resolution and high cost. Therefore, it is necessary to explore a change detection method combing low- and high-resolution images to acquire urban change areas more accurately and quickly. In this paper, an end-to-end siamese generative adversarial network (SiamGAN) integrating a super resolution network and the siamese structure was proposed for change detection under different scales. The super-resolution network is used to reconstruct low-resolution images into high-resolution images, while the siamese structure is adopted as the classification network to detect changes. In the experiments, SiamGAN achieved an F1 of 76.06% and an IoU of 61.52% in the test set, which is respectively 5.68% and 6.92% higher than the CNN-based methods using LR images after bicubic interpolation. The results show that our proposed method can effectively overcome difference in scale between low- and high-resolution images and perform change detection more precisely and rapidly.

Urban villages (UVs) are distinctive products formed in the process of rapid urbanization. The fine-grained mapping of UVs from satellite images has always been a considerable challenge because of the complex urban structures and the insufficiency of labeled samples. In this letter, we propose using the domain adaptation strategy to tackle the domain shift problem by employing adversarial learning to tune the semantic segmentation network so as to adaptively obtain similar outputs for input images from different domains. The proposed method was coupled with several segmentation networks, including U-Net, RefineNet, and DeepLab v3+, and the results show that domain adaptation can significantly improve the pixel-level mapping of UVs.