This paper proposes a technique to estimate the distance between an object and a rolling shutter camera using a single image. The implementation of this technique uses the principle of the rolling shutter effect (RSE), a distortion within the rolling-shutter-type camera. The proposed technique has a mathematical strength compared to other single photo-based distance estimation methods that do not consider the geometric arrangement. The relationship between the distance and RSE angle was derived using the camera parameters (focal length, shutter speed, image size, etc.). Mathematical equations were derived for three different scenarios. The mathematical model was verified through experiments using a Nikon D750 and Nikkor 50 mm lens mounted on a car with varying speeds, object distances, and camera parameters. The results show that the mathematical model provides an accurate distance estimation of an object. The distance estimation error using the RSE due to the change in speed remained stable at approximately 10 cm. However, when the distance between the object and camera was more than 10 m, the estimated distance was sensitive to the RSE and the error increased dramatically.

In this paper, we propose a method to regenerate Rational Polynomial Coefficients (RPCs) using KOMPSAT-3A imagery and to reduce the geolocation error using minimum ground control points (GCPs). To estimate the new RPCs, the physical sensor model fitted to KOMPSAT-3A imagery was utilized and virtual GCPs over the study area were created. The size of the virtual grid used was 20x20x20. To remove the sensor-related errors in physical sensor model, three different image correction models (image coordinate translation model, shift and drift model, and affine transformation model) were additionally applied. We evaluated our proposed method in two areas within Korea, one in urban (Seoul) and one in rural (Goheung) areas. The results showed that there was a significant improvement after applying the suggested approach in the two areas. The image coordinate translation model is suggested in terms of GCP requirement and expected errors estimated from the error propagation analysis using Gauss–Markov Model (GMM).

Landslides are one of the critical natural hazards that cause human, infrastructure, and economic losses. Risk of catastrophic losses due to landslides is significant given sprawled urban development near steep slopes and the increasing proximity of large populations to hilly areas. For reducing these losses, a high-resolution digital terrain model (DTM) is an essential piece of data for a qualitative or a quantitative investigation of slopes that may lead to landslides. Data acquired by a terrestrial laser scanning (TLS), called a point cloud, has been widely used to generate a DTM, since a TLS is appropriate for detecting small- to large-scale ground features on steep slopes. For an accurate DTM, TLS data should be filtered to remove non-ground points, but most current algorithms for extracting ground points from a point cloud have been developed for airborne laser scanning (ALS) data and not TLS data. Moreover, it is a challenging task to generate an accurate DTM from a steep-slope area by using existing algorithms. For these reasons, we developed an algorithm to automatically extract only ground points from the point clouds of steep terrains. Our methodology is focused on TLS datasets and utilizes the adaptive principal component analysis–triangular irregular network (PCA-TIN) approach. Our method was applied to two test areas and the results showed that the algorithm can cope well with steep slopes, giving an accurate surface model compared to conventional algorithms. Total accuracy values of the generated DTMs in the form of root mean squared errors are 1.84 cm and 2.13 cm over the areas of 5252 m² and 1378 m², respectively. The slope-based adaptive PCA-TIN method demonstrates great potential for TLS-derived DTM construction in steep-slope landscapes.

In smart cities, a large amount of optical camera equipment is deployed and used. Closed-circuit television (CCTV), unmanned aerial vehicles (UAVs), and smartphones are some examples of such equipment. However, additional information about these devices, such as 3D position, orientation information, and principal distance, is not provided. To solve this problem, the structured mobile mapping system point cloud was used in this study to investigate methods of estimating the principal point, position, and orientation of optical sensors without initial given values. The principal distance was calculated using two direct linear transformation (DLT) models and a perspective projection model. Methods for estimating position and orientation were discussed, and their stability was tested using real-world sensors. When the perspective projection model was used, the camera position and orientation were best estimated. The original DLT model had a significant error in the orientation estimation. The correlation between the DLT model parameters was thought to have influenced the estimation result. When the perspective projection model was used, the position and orientation errors were 0.80 m and 2.55°, respectively. However, when using a fixed-wing UAV, the estimated result was not properly produced owing to ground control point placement problems.

High-resolution pushbroom satellites routinely acquire multi-tenskilometer-scale strips whose vendors’ rational polynomial coefficients (RPCs) exhibit systematic, direction-dependent biases that accumulate downstream when ground control is sparse. This study presents a physically interpretable stripwise extrapolation framework that predicts along- and across-track RPC correlation coefficients for inaccessible segments from an upstream calibration subset. Terrain-independent RPCs were regenerated and residual image-space errors were modeled with weighted least squares using elapsed time, off-nadir evolution, and morphometric descriptors of the target terrain. Gaussian kernel weights favor calibration scenes with a Jarque–Bera-indexed relief similar to the target. When applied to three KOMPSAT-3A panchromatic strips, the approach preserves native scene geometry while transporting calibrated coefficients downstream, reducing positional errors in two strips to <2.8 pixels (~2.0 m at 0.710 m Ground Sample Distance, GSD). The first strip with a stronger attitude drift retains 4.589 pixel along-track errors, indicating the need for wider predictor coverage under aggressive maneuvers. The results clarify the directional error structure with a near-constant across-track bias and low-frequency along-track drift and show that a compact predictor set can stabilize extrapolation without full-block adjustment or dense tie networks. This provides a GCP-efficient alternative to full-block adjustment and enables accurate georeferencing in controlled environments.