This paper provides a comprehensive review of recent advances in object detection in optical remote sensing images (RSIs). Object detection in optical RSIs involves identifying and locating objects of interest in aerial or satellite images. The paper discusses various methods, including template matching-based, knowledge-based, object-based image analysis (OBIA)-based, and machine learning-based approaches. It also covers five publicly available datasets and three standard evaluation metrics. The paper highlights the challenges in current studies and proposes two promising research directions: deep learning-based feature representation and weakly supervised learning-based geospatial object detection. The review covers about 270 publications and discusses different object categories, including road, building, tree, vehicle, ship, airport, and urban-area. The paper emphasizes the importance of object detection in various applications such as environmental monitoring, geological hazard detection, land use/land cover mapping, geographic information system (GIS) update, precision agriculture, and urban planning. The paper also discusses the evolution of remote sensing technology and the increasing availability of high-resolution satellite and aerial images, which have enabled more detailed spatial and textural information. The paper reviews various methods for object detection, including template matching, knowledge-based approaches, OBIA-based methods, and machine learning-based methods. It also discusses the challenges in current studies and proposes future research directions. The paper is organized into sections that cover the taxonomy of methods, template matching-based object detection, knowledge-based object detection, OBIA-based object detection, and machine learning-based object detection. The paper concludes with a discussion of the challenges and future research directions in the field of object detection in optical RSIs.This paper provides a comprehensive review of recent advances in object detection in optical remote sensing images (RSIs). Object detection in optical RSIs involves identifying and locating objects of interest in aerial or satellite images. The paper discusses various methods, including template matching-based, knowledge-based, object-based image analysis (OBIA)-based, and machine learning-based approaches. It also covers five publicly available datasets and three standard evaluation metrics. The paper highlights the challenges in current studies and proposes two promising research directions: deep learning-based feature representation and weakly supervised learning-based geospatial object detection. The review covers about 270 publications and discusses different object categories, including road, building, tree, vehicle, ship, airport, and urban-area. The paper emphasizes the importance of object detection in various applications such as environmental monitoring, geological hazard detection, land use/land cover mapping, geographic information system (GIS) update, precision agriculture, and urban planning. The paper also discusses the evolution of remote sensing technology and the increasing availability of high-resolution satellite and aerial images, which have enabled more detailed spatial and textural information. The paper reviews various methods for object detection, including template matching, knowledge-based approaches, OBIA-based methods, and machine learning-based methods. It also discusses the challenges in current studies and proposes future research directions. The paper is organized into sections that cover the taxonomy of methods, template matching-based object detection, knowledge-based object detection, OBIA-based object detection, and machine learning-based object detection. The paper concludes with a discussion of the challenges and future research directions in the field of object detection in optical RSIs.