Given the large volume of remote sensing images collected daily, automatic object detection and segmentation have been a consistent need in Earth observation (EO). However, objects of interest vary in shape, size, appearance, and reflecting properties. This is not only reflected by the fact that these objects exhibit differences due to their geographical diversity but also by the fact that these objects appear differently in images collected from different sensors (optical and radar) and platforms (satellite, aerial, and unmanned aerial vehicles (UAV)). Although there exists a plethora of object detection methods in the area of remote sensing, given the very fast development of prevalent deep learning methods, there is still a lack of recent updates for object detection methods. In this paper, we aim to provide an update that informs researchers about the recent development of object detection methods and their close sibling in the deep learning era, instance segmentation. The integration of these methods will cover approaches to data at different scales and modalities, such as optical, synthetic aperture radar (SAR) images, and digital surface models (DSM). Specific emphasis will be placed on approaches addressing data and label limitations in this deep learning era. Further, we survey examples of remote sensing applications that benefited from automatic object detection and discuss future trends of the automatic object detection in EO.

Remote Sensing Object Detection in the Deep Learning Era—A Review

Remote sensing (RS) techniques are essential for studying hazardous landslide events because they capture information and monitor sites at scale. They enable analyzing causes and impacts of ongoing events for disaster management. There has been a plethora of work in the literature mostly discussing (1) applications to detect, monitor, and predict landslides using various instruments and image analysis techniques, (2) methodological mechanics in using optical and microwave sensing, and (3) quantification of surface geological and geotechnical changes using 2D images. Recently, studies have shown that the degree of hazard is mostly influenced by speed, type, and volume of surface deformation. Despite available techniques to process lidar and image/radar-derived 3D geometry, prior works mostly focus on using 2D images, which generally lack details on the 3D aspects of assessment. Thus, assessing the 3D geometry of terrain using elevation/depth information is crucial to determine its cover, geometry, and 3D displacements. In this review, we focus on 3D landslide analysis using RS data. We include (1) a discussion on sources, types, benefits, and limitations of 3D data, (2) the recent processing methods, including conventional, fusion-based, and artificial intelligence (AI)-based methods, and (3) the latest applications.

Remote Sensing-Based 3D Assessment of Landslides: A Review of the Data, Methods, and Applications

This study aims to accurately determine the distribution of landslides in the complex terrain of Jiangdingya, Nanyu Township, Zhouqu County, Gansu Province. The digital orthophoto map (DOM) and digital elevation model (DEM) are used to accurately identify landslide areas and analyze associated data. Based on image-based supervised classification, the influence factor constraint analysis is used to further identify and delineate the landslide area. Three mathematical morphology operations—erosion, dilation, and opening—are then applied to automatically identify and extract landslides. Experimental results demonstrate that achieving an accuracy, precision, and recall of 98.02%, 85.24%, and 84.78% shows that it is possible to better avoid interference caused by complex terrain with rich features. High-resolution DEM and DOM data contain rich spectral and texture information. These data can accurately depict geomorphic features of complex terrain and aid in identifying landslide-prone areas when combined with mathematical morphology processing. This contribution is important for identifying landslides in complex terrain and emergency disaster management. 

Automatic Extraction Method of Landslide Based on Digital Elevation Model and Digital Orthophoto Map Data Combined in Complex Terrain

An empirical evaluation of the Random Forests classifier models for variable selection in a large-scale lung cancer case-control study

Multi-view satellite images play a crucial role in reconstructing Earth's surface information. Its application in deep learning-based Multi-View Stereo (MVS) methods has recently exhibited impressive performance. However, there is still ample room for improvement, particularly in the reconstruction of the Digital Surface Model (DSM) within scenes characterized by dense features, high repetition, and significant elevation variations. To address the challenges, we present a novel network, Appli-SatNet. The network initially introduces a multi-scale Transformer module to emphasize feature correlations within and across multiple images. Subsequently, a novel Gated Recurrent Units (GRU) iterative optimizer is introduced to derive iterative indices for the cost volume within implicit states. This iterative process enhances the correlation among the cost volumes and enables multiple iterative refinements for prediction elevation maps. Ultimately, this leads to the generation of a high-resolution DSM that matches the resolution of the nadir image. The method is validated on a publicly available TLC SatMVS dataset, which includes plain areas with repetitive textures and inconspicuous features, mountainous regions with significant continuous elevation changes, and urban areas with dramatic elevation variations and high repetition. Comparative to mainstream methods, our approach demonstrated remarkable improvements in reconstruction quality. Moreover, when applied to low-resolution images captured by the Ziyuan-3 (ZY-3) 01 satellite over various complex terrains, it consistently exhibits robust reconstruction accuracy and superior visual quality, underscoring the applicability of our model. 

Highly Applicable Iterative Network for 3D Reconstruction Based on Multi-View Satellite Images

The current practice in land cover/land use change analysis relies heavily on the individually classified maps of the multitemporal data set. Due to varying acquisition conditions (e.g., illumination, sensors, seasonal differences), the classification maps yielded are often inconsistent through time for robust statistical analysis. 3D geometric features have been shown to be stable for assessing differences across the temporal data set. Therefore, in this article we investigate he use of a multitemporal orthophoto and digital surface model derived from satellite data for spatiotemporal classification. Our approach consists of two major steps: generating per-class probability distribution maps using the random-forest classifier with limited training samples, and making spatiotemporal inferences using an iterative 3D spatiotemporal filter operating on per-class probability maps. Our experimental results demonstrate that the proposed methods can consistently improve the individual classification results by 2%-6% and thus can be an important postclassification refinement approach.

3D Iterative Spatiotemporal Filtering for Classification of Multitemporal Satellite Data Sets

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3D Iterative Spatiotemporal Filtering for Classification of Multitemporal Satellite Data Sets

3D Iterative Spatiotemporal Filtering for Classification of Multitemporal Satellite Data Sets