Extracting roads from complex high-resolution remote sensing images to update road networks has become a recent research focus. How to apply the contextual spatial correlation and topological structure of the roads properly to improve the extraction accuracy becomes a challenge in the increasingly complex road environment. In this paper, inspired by the prior knowledge of the road shape and the progress in deformable convolution, we proposed a road augmented deformable attention network (RADANet) to learn the long-range dependencies for specific road pixels. We developed a road augmentation module (RAM) to capture the semantic shape information of the road from four strip convolutions. Deformable attention module (DAM) combines the sparse sampling capability of deformable convolution with the spatial self-attention mechanism. The integration of RAM enables DAM to extract road features more specifically. Furthermore, RAM is placed behind the fourth stage of encoder, and DAM is placed between last four stages of encoder and decoder in RADANet to extract multi-scale road semantic information. Comprehensive experiments on representative public datasets (DeepGlobe and CHN6-CUG road datasets) demonstrate that our RADANet achieves advanced results compared with the state-of-the-art methods.

The existing buffers algorithms cannot effectively to meet the demands of high accuracy of buffer analysis in practice although many efforts have been made in the past 60 years. A generalized buffering algorithm (GBA) is presented, which considers the geometric distance and the attribute characteristics of all instances within buffer zone. The proposed algorithm includes three major steps: (1) select and initialize target instance; (2) determine buffer boundary points through mining homogeneous pattern; (3) “smoothly” connect buffer boundary points to generate the generalized buffer zone. The details for the generations of the generalized point buffer (GPIB) zone, the generalized line buffer (GLB) zone, and the generalized polygon buffer (GPLB) zone are discussed. Two dataset are used to validate the performances of the proposed GBA. Six parameters are applied as indexes to evaluate the proposed algorithm. The experimental results discovered that (1) the GBA is close to the tradition buffering algorithm (TBA) when the angle increment ( $\Delta \varphi$ ) in GPIB, line increment ( $\Delta L$ ) in GLB, and arc length increment ( $\Delta S$ ) in GPLB approach to zero, respectively; (2) the proposed GBA can accurately reflect the real situation of the buffering zone, and improve the deficiency and accuracy of TBA in real application.

Deformable convolutional networks (DCNs) can mitigate the inherent limited geometric transformation. We reformulate the spatialwise attention mechanism using DCNs in this article for semantic segmentation of high-resolution remote sensing (HRRS) images. It combines the sparse spatial sampling strategy and the long-range relationship modeling capability, namely, deformable attention module (DAM). Such locality awareness, more adaptable to HRRS image structures, can capture each pixel’s neighboring structural information. A reasonable multiscale deformable attention net (MDANet) is designed for the HRRS image semantic segmentation with a slightly increased computational cost based on the proposed DAM. Specifically, standard convolutional layers in the raw ResNet50 are equipped with a DAM to control sampling over a broader range of feature levels and aggregate multiscale context information. The experimental results evaluated on Vaihingen and DeepGlobe Land Cover Classification datasets show that the performance accuracy of MDANet is improved by 7.77% and 8.45% compared with the backbone network (ResNet50) in terms of Miou evaluation, respectively. Furthermore, a DAM can perform better than a global spatial attention mechanism with less computation on the $3 \times 64 \times 64$ feature map. In addition, the added ablation studies demonstrate the effectiveness and efficiency of the DAM and multiscale strategy, respectively. Moreover, the sensitivity of critical hyperparameters is analyzed.

The traditional ortho-rectification technique for remotely sensed (RS) images, which is performed on the basis of a ground image processing platform, has been unable to meet timeliness or near timeliness requirements. To solve this problem, this paper presents research on an ortho-rectification technique based on a field programmable gate array (FPGA) platform that can be implemented on board spacecraft for (near) real-time processing. The proposed FPGA-based ortho-rectification method contains three modules, i.e., a memory module, a coordinate transformation module (including the transformation from geodetic coordinates to photo coordinates, and the transformation from photo coordinates to scanning coordinates), and an interpolation module. Two datasets, aerial images located in central Denver, Colorado, USA, and an aerial image from the example dataset of ERDAS IMAGINE 9.2, are used to validate the processing speed and accuracy. Compared to traditional ortho-rectification technology, the throughput from the proposed FPGA-based platform and the personal computer (PC)-based platform are 11,182.3 kilopixels per second and 2582.9 kilopixels per second, respectively. This means that the proposed FPGA-based platform is 4.3 times faster than the PC-based platform for processing the same RS images. In addition, the root-mean-square errors of the planimetric coordinates φ_X and φ_Y and the distance φ_S are 1.09 m, 1.61 m, and 1.93 m, respectively, which can meet the requirements of correction accuracy in practice.

Textured 3D mesh is one of the final user products in photogrammetry and remote sensing. However, research on the semantic segmentation of complex urban scenes represented by textured 3D meshes is in its infancy. We present a mesh-based dynamic graph CNN (DGCNN) for the semantic segmentation of textured 3D meshes. To represent each mesh facet, composite input feature vectors are constructed by concatenating the face-inherent features, i.e., XYZ coordinates of the center of gravity (CoG), texture values, and normal vectors. A texture fusion module is embedded into the proposed mesh-based DGCNN to generate high-level semantic features of the high-resolution texture information, which is useful for semantic segmentation. We achieve competitive accuracies when the proposed method is applied to the SUM mesh datasets. The overall accuracy (OA), Kappa coefficient (Kap), mean precision (mP), mean recall (mR), mean F1 score (mF1), and mean intersection over union (mIoU) are 93.3%, 88.7%, 79.6%, 83.0%, 80.7%, and 69.6%, respectively. In particular, the OA, mean class accuracy (mAcc), mIoU, and mF1 increase by 0.3%, 12.4%, 3.4%, and 6.9%, respectively, compared to the state-of-the-art method.

Recent approaches based on Neural Radiance Fields (NeRF) showcase remarkable results in 3D reconstruction of small-scale scenes by encoding volume density and color observations using implicit functions. However, when confronted with complex and diverse large-scale scenes, it invariably experiences issues such as blurry textures and missing details. In this work, we present a superpixel-based neural radiance field (named SuperNeRF), an additional loss for learning radiance fields that takes advantage of superpixels texture constraints. Building upon NeRF, we leverage superpixels to establish spatial consistency constraints, enabling the precise extraction of 3D geometry and appearance for large-scale scenes. SuperNeRF is capable of guiding locally adjacent and similar pixels to form nearly consistent ray termination distributions, and it is compatible with the state-of-the-art NeRF-based methods. Comprehensive experiments conducted on representative aviation and aerospace datasets demonstrate that our SuperNeRF exhibits a significant superiority in accuracy over state-of-the-art methods. Code will be available at https://github.com/xczbecalm/supernerf.