Road markings are reflective features on roads that provide important information for safe and smooth driving. With the rise of autonomous vehicles (AV), it is necessary to represent them digitally, such as in high-definition (HD) maps generated by mobile mapping systems (MMSs). Unfortunately, MMSs are expensive, paving the way for the use of low-cost alternatives such as low-cost light detection and ranging (LiDAR) sensors. However, low-cost LiDAR sensors produce sparser point clouds than their survey-grade counterparts. This significantly reduces the capabilities of existing deep learning techniques in automatically extracting road markings, such as using convolutional neural networks (CNNs) to classify point cloud-derived imagery. A solution would be to provide a more suitable loss function to guide the CNN model during training to improve predictions. In this work, we propose a modified loss function—focal combo loss—that enhances the capability of a CNN to extract road markings from sparse point cloud-derived images in terms of accuracy, reliability, and versatility. Our results show that focal combo loss outperforms existing loss functions and CNN methods in road marking extractions in all three aspects, achieving the highest mean F1-score and the lowest uncertainty for the two distinct CNN models tested.

High-definition (HD) maps for autonomous driving rely on data from mobile mapping systems (MMS), but the high cost of MMS sensors has led researchers to explore cheaper alternatives like low-cost LiDAR sensors. While cost effective, these sensors produce sparser point clouds, leading to poor feature representation and degraded performance in deep learning techniques, such as convolutional neural networks (CNN), for tasks like road marking extraction and classification, which are essential for HD map generation. Examining common image segmentation workflows and the structure of U-Net, a CNN, reveals a source of performance loss in the succession of resizing operations, which further diminishes the already poorly represented features. Addressing this, we propose improving U-Net’s ability to extract and classify road markings from sparse-point-cloud-derived images by introducing a learnable resizer (LR) at the input stage and learnable resizer blocks (LRBs) throughout the network, thereby mitigating feature and localization degradation from resizing operations in the deep learning framework. Additionally, we incorporate Laplacian filters (LFs) to better manage activations along feature boundaries. Our analysis demonstrates significant improvements, with F1-scores increasing from below 20% to above 75%, showing the effectiveness of our approach in improving road marking extraction and classification from sparse-point-cloud-derived imagery.

With the rise of self-driving cars, an increasing number of vehicles are equipped with low-cost light detection and ranging (LiDAR) sensors that could potentially serve as a massive mobile mapping resource, particularly for jobs that require multiple and frequent scanning, such as maintaining dynamic high-definition maps or digital twins. However, low-cost LiDAR sensors produce sparser point clouds during scanning which can make deep learning techniques for the automatic retrieval of features difficult like extracting road markings. In this work, we aim to improve the performance of a convolutional neural network (CNN) model for road marking extraction from sparse mobile LiDAR scanning (MLS) point cloud-derived images. We propose the modification of the Fast-SCNN model structure by adding a 2D convolution branch with masking in the feature fusion step: MFSCNN. To retain speed we only use MFSCNN to boost model training and still utilize Fast-SCNN for inference. Our results indicate potential, with a 4.6% increase in mean f1-score and an 8% decrease in uncertainty for the road marking class after multiple trials. Additionally, this research aims to support and increase research interest in lower-cost LiDARs for mobile mapping.

Recent works have attempted to extract features such as road markings from sparse mobile LiDAR scanning point cloud-derived images via convolutional neural networks (CNN). In this paper, the use of such methods for ground segmentation was explored. To begin, point clouds from each channel will be projected onto the y-z plane to generate the images that will be used for training and testing the CNN model. Then, for the main workflow, the following steps were performed for each channel: (1) point cloud-to-image conversion; (2) CNN classification; and (3) image-to-point cloud projection. Then utilizing multi-threading, each channel is processed in parallel to generate our ground-segmented sparse point cloud. Our findings have shown successful ground segmentation, achieving an f1-score of 98.9%. However, it performed 27.81% slower as compared to RANSAC. Overall, this initial investigation has demonstrated that ground segmentation from sparse point cloud-derived imagery is possible, and with further improvements to the CNN model, to make it faster, it has good potential to act as an alternative to conventional point cloud processing.

ISPRS Student Consortium (ISPRS SC) has played a significant role in bringing students and young researchers closer to the field of remote sensing and photogrammetry in the evolving landscape of geospatial sciences. Innovative strategies and events have been employed by the ISPRS SC to address contemporary challenges and harness emerging opportunities in the field of photogrammetry and remote sensing. Through an analysis of the global contributions of ISPRS SC, ranging from research initiatives to the online webinar series, this paper underscores the consortium's commitment to fostering collaboration, innovation, and knowledge exchange among young scholars and professionals worldwide. The paper highlights key thematic areas addressed by ISPRS SC such as advancements in remote sensing technologies, geospatial data analysis, and applications in diverse domains including environmental monitoring, agriculture, urban planning, and disaster management. Moreover, in this paper, we emphasize the role of the consortium in nurturing future leaders in geospatial sciences through ISPRS SC mentorship programs, networking opportunities, and capacity-building initiatives. By highlighting the initiatives led by the ISPRS Student Consortium, this paper underscores the key role of youth-driven innovation for positive change and shaping the future of geospatial sciences on a global scale.

In accordance with the United Nations (UN) Sustainable Development Goal (SDG) 16: Peace, Justice, and Strong Institutions, this study explores ship monitoring through the use of Synthetic Aperture Radar (SAR) for its potential applications to economic and security purposes. One method to extract ships through SAR-derived imagery is to employ the use of convolutional neural networks (CNN). However, the extraction of small features continues to be a challenging task for CNNs. To improve the performance in such cases, one way is to employ the use of an appropriate loss function, which helps guide the CNN model during training. In this paper, Focal Combo (FC) loss, a recent loss function designed for extreme class imbalance, will be investigated to analyze its effects when applied to ship extraction. In doing so, this paper also presents a thorough comparison of existing loss functions in their capability to segment and detect ships on SAR imagery. Making use of the U-Net model, our results demonstrate that by using FC loss we can observe an increase in segmentation of about 9% in terms of f3-score and a decrease in missed detections by about 17 ships (after post-processing) when compared to cross-entropy loss. Unfortunately, it has also shown a significant drop in precision of about 35% resulting in an additional 270 ships being incorrectly detected in the background. In future work, varying CNN models shall be tested to see if the pattern persists and several trials shall be conducted to assess consistency.