Many contour-based image corner detectors are based on the curvature scale-space (CSS). We identify the weaknesses of the CSS-based detectors. First, the ldquocurvaturerdquo itself by its ldquodefinitionrdquo is very much sensitive to the local variation and noise on the curve, unless an appropriate smoothing is carried out beforehand. In addition, the calculation of curvature involves derivatives of up to second order, which may cause instability and errors in the result. Second, the Gaussian smoothing causes changes to the curve and it is difficult to select an appropriate smoothing-scale, resulting in poor performance of the CSS corner detection technique. We propose a complete corner detection technique based on the chord-to-point distance accumulation (CPDA) for the discrete curvature estimation. The CPDA discrete curvature estimation technique is less sensitive to the local variation and noise on the curve. Moreover, it does not have the undesirable effect of the Gaussian smoothing. We provide a comprehensive performance study. Our experiments showed that the proposed technique performs better than the existing CSS-based and other related methods in terms of both average repeatability and localization error.

Automatic extraction of building roofs from remote sensing data is important for many applications, including 3D city modeling. This paper proposes a new method for automatic segmentation of raw LIDAR (light detection and ranging) data. Using the ground height from a DEM (digital elevation model), the raw LIDAR points are separated into two groups. The first group contains the ground points that form a “building mask”. The second group contains non-ground points that are clustered using the building mask. A cluster of points usually represents an individual building or tree. During segmentation, the planar roof segments are extracted from each cluster of points and refined using rules, such as the coplanarity of points and their locality. Planes on trees are removed using information, such as area and point height difference. Experimental results on nine areas of six different data sets show that the proposed method can successfully remove vegetation and, so, offers a high success rate for building detection (about 90% correctness and completeness) and roof plane extraction (about 80% correctness and completeness), when LIDAR point density is as low as four points/m2. Thus, the proposed method can be exploited in various applications.

The rapid growth of the global population has placed immense pressure on agriculture to enhance food production while addressing environmental and socioeconomic challenges such as biodiversity loss, water scarcity, and climate variability. Addressing these challenges requires adopting modern techniques and advancing agricultural research. Although some techniques, such as machine learning and deep learning, are increasingly used in agriculture, progress is constrained by the lack of large labelled datasets. This constraint arises because collecting data is often time-consuming, labour-intensive, and requires expert knowledge for data annotation. To mitigate data limitations, transfer learning (TL) offers a viable solution by allowing pre-trained models to be adapted for agricultural applications. Many researchers have demonstrated TL’s potential to advance agriculture. Despite its importance, there is a lack of a comprehensive review, which could be essential to guide researchers in this field. Given the significance and the lack of a review paper, this paper provides a review dedicated to TL in agriculture, offering three main contributions. First, we provide an in-depth background study on TL and its applications in agriculture. Second, we offer a comprehensive examination of TL-based agricultural applications, covering pre-trained models, dataset sources, input image types, implementation platforms, and TL approaches. Third, based on an exploration of the existing studies, we identify the challenges faced when applying TL in agriculture. Finally, to address the identified challenges, we recommend suggestions for future research directions.

Rectilinear building outline generation from the point set of a building usually works in three steps. Boundary edges that constitute the building outline are first identified. A sequence of points is then traced from the edges to define the building boundary. Finally, lines are generated from the sequence of points and adjusted to form a regular building outline. Existing solutions have shortcomings in one or more of the following cases: identifying details along a concave shape, separate identification of a ‘hole’ inside the shape, proper boundary tracing, and preservation of detailed information along a regularized building outline. This article proposes new solutions to all three steps. By using the maximum point-to-point distance in the input data, the solution to the identification step properly detects the boundary edges for any type of shape and separately recognizes holes, if any, inside the shape. The proposed tracing algorithm divides boundary edges into segments, accurately obtains the sequence of points for each segment and then merges them, if necessary, to produce a single boundary for each shape. The regularization step proposes an improved corner and line extraction algorithm and adjusts the extracted lines with respect to the automatically determined principal directions of buildings. In order to evaluate the performance, an evaluation system that makes corner correspondences between an extracted building outline and its reference outline is also proposed. Experimental results show that the proposed solutions can preserve detail along the building boundary and offer high pixel-based completeness and geometric accuracy, even in low-density input data.