Urban Villages (UVs) renovation has been incorporated into the Sustainable Development Goals (SDGs) as a result of the inequality issue among residents garnering substantial social attention. However, existing deep-learning techniques for UVs extraction have been limited to a single spatial scale (e.g., patch-level or pixel-level extraction), leading to inadequate precision and integrity in their extraction outcomes. To overcome this limitation, our study introduces HR-UVFormer, a top-down and multimodal hierarchical extraction approach that extracts UVs from a coarse scale (patch) to a fine granularity (pixel), aiming to enhance the internal completeness and boundary accuracy of the extraction results. The multimodal approach can effectively fuse multimodal features (e.g., building footprints (BF)) with remote sensing images (RSI) to enhance UVs extraction. The Shenzhen results indicate that the coarse-scale extraction accuracy achieves an overall accuracy (OA) of 98.79%, and the fine-grained extraction accuracy achieves a mean Intersection over Union (mIoU) of 93.60%. Furthermore, ablation experiments demonstrate a notable 7.14% improvement in mIoU with the hierarchical extraction strategy compared to the traditional pixel-based extraction strategy, and the fusion of BF and RSI yields further improvements of 2.78% and 0.65% in OA and mIoU, respectively. This finding confirms the synergistic effect between RSI and BF in UVs extraction, which has been further analyzed in this study. Additionally, the proposed model outperforms other deep learning models and exhibits the potential to support more modal features (e.g., POI). Finally, the experimental dataset and code can be publicly accessed at https://github.com/q1310546582/HR-UVFormer-code.

Change detection (CD) in high-resolution remote sensing has received large attention due to its wide range of applications. Many methods have been proposed in the literature and achieved excellent performance. However, they are often fully supervised, thus requiring abundant pixel-level labeled samples, which is time-consuming and labor-intensive. Especially compared to the common single-temporal interpretation, labeling bi-temporal images is often more complicated. Therefore, this study combines weakly supervised learning (WSL) to reduce label acquisition costs. But changed regions are small, fragmented, and similar to the background, which increase the gap between weakly supervised and fully supervised tasks. To address these difficulties, we explore self-supervised methods to construct a WSL framework based on image-level labels for general CD, termed WSLCD in this paper. First, we design a double-branch siamese network to derive embeddings and initial class attention maps (CAMs), which inputs the original image pair and the spatially transformed image pair. Second, mutual learning and equivariant regularization (MLER) is enforced on CAMs from different views, which implements consistency constraints in confusion regions and makes CAMs learn from each other based on saliency regions. Furthermore, prototype-based contrastive learning (PCL) is designed such that unreliable pixels can learn from prototypes computed from reliable pixels. PCL includes intra-view contrast and cross-view contrast depending on whether the prototypes and class embeddings are from the same view. With the above strategies, we narrow the gap between image-level weakly supervised CD and fully supervised CD. Experiments are conducted on three CD datasets, including CLCD, DSIFN and GCD. Our method achieves state-of-the-art performance on pseudo label generation and CD. The code is available at https://github.com/mfzhao1998/WSLCD.

With the development of high-resolution satellites, more and more attention has been paid to remote sensing (RS) scene classification. Convolutional neural networks (CNNs), which replace the traditional handcrafted features with a learning-based feature extraction mechanism, are widely used in scene classification. But CNNs are less effective in deriving long-range contextual relations, which limits the further improvement. Visual transformer (VT), an emerging image processing method, provides a new perspective for RS scene classification by directly acquiring long-range features. Although there have been limited works combining CNN and VT through simple concatenation, the collaborations between them are insufficient. To address these issues, we propose a local and long-range collaborative framework (L2RCF). First, we design a dual-stream structure to extract the local and long-range features. Second, a cross-feature calibration (CFC) module is designed for them to improve representation of the fusion features. Then, combining deep supervision (DS) and deep mutual learning (DML), a novel joint loss is proposed to enhance the dual-stream feature extractor and further improve the fused features. Finally, a two-stage semi-supervised training strategy is designed to improve performance with unlabeled samples. To demonstrate the effectiveness of L2RCF, we conducted experiments on three widely used RS scene classification data sets: RSSCN7, AID, and NWPU. The results show that L2RCF performs significantly better compared with some state-of-the-art scene classification methods.

In the area of optical remote sensing image processing, object detection is among the utmost essential and difficult tasks. By virtue of the excellent feature representation abilities of deep convolutional neural networks (DCNNs), the performance of remote sensing object detection has recently increased dramatically. However, DCNN-based methods necessitate sufficient labeled training samples and tend to experience a considerable performance fall when training examples are insufficient. Many of the recently developed few-shot object detection (FSOD) methods attempt to solve the issue by using the idea of meta-learning, which intends to extract knowledge that can be generalized across various tasks. Despite its success, the negligence of the knowledge learned in the past, and inter-class correlations hinder the detection ability of novel classes. In this article, we propose a new meta-memory based FSOD approach named MM-RCNN. Specifically, MM-RCNN adopts a memory moduel to store each category’s knowledge learned in the training stage and the memory-based external attention (MEA) to aggregate all categories’ information simultaneously. Based on MEA, we design two feature enhancement modules for the region proposal network (RPN) and detection head to boost the performance. Experiments over two remote sensing benchmarks, i.e., DIOR and NWPU VHR10, verify the capability of our method (0.239 and 0.557 average mAP across all settings).

UNLABELLED: We created a fast, robust and general C+ + implementation of a single-nucleotide polymorphism (SNP) set enrichment algorithm to identify cell types, tissues and pathways affected by risk loci. It tests trait-associated genomic loci for enrichment of specificity to conditions (cell types, tissues and pathways). We use a non-parametric statistical approach to compute empirical P-values by comparison with null SNP sets. As a proof of concept, we present novel applications of our method to four sets of genome-wide significant SNPs associated with red blood cell count, multiple sclerosis, celiac disease and HDL cholesterol. AVAILABILITY AND IMPLEMENTATION: http://broadinstitute.org/mpg/snpsea. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Polarization space-borne sensor, just like POLDER (Polarization and Directionality of the Earth's Reflectances), is a new instrument devoted to the global observation of solar radiation reflected by the Earth surface-atmosphere system. It is necessary to acquire polarized information in retrieval of aerosol properties over land surface. Often the aerosol contribution is small compared to the surface, especially by covered vegetation. Atmospheric scattering is much more polarized than the surface reflectance. Using polarized information could solve the inverse problem of separating the surface and atmospheric scattering contributions. This paper presents retrieval of aerosols properties from multi-wavelength polarized measurements. The results suggest that it is feasible and possibility for discriminating the aerosol contribution from the surface in the aerosol retrieval procedure using multidirectional and multiwavelegth polarization measurements.