The Automatic Extraction of Roads from LIDAR data

A method for the automatic detection of roads from airborne laser scanner data is presented. Traditionally, intensity information has not been used in feature extraction from LIDAR data because the data is too noisy. This article deals with using as much of the recorded laser information as possible thus both height and intensity are used. To extract roads from a LIDAR point cloud, a hierarchical classification technique is used to classify the LIDAR points progressively into road or non-road. Initially, an accurate digital terrain model (DTM) model is created by using successive morphological openings with different structural element sizes. Individual laser points are checked for both a valid intensity range and height difference from the subsequent DTM. A series of filters are then passed over the road candidate image to improve the accuracy of the classification. The success rate of road detection and the level of detail of the resulting road image both depend on the resolution of the laser scanner data and the types of roads expected to be found. The presence of road-like features within the survey area such as private roads and car parks is discussed and methods to remove this information are entertained. All algorithms used are described and applied to an example urban test site

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

A Platform for Proactive, Risk-Based Slope Asset Management, Phase II

INE/AUTC 15.0

CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR

We introduce a novel method for odometry estimation using convolutional neural networks from 3D LiDAR scans. The original sparse data are encoded into 2D matrices for the training of proposed networks and for the prediction. Our networks show significantly better precision in the estimation of translational motion parameters comparing with state of the art method LOAM, while achieving real-time performance. Together with IMU support, high quality odometry estimation and LiDAR data registration is realized. Moreover, we propose alternative CNNs trained for the prediction of rotational motion parameters while achieving results also comparable with state of the art. The proposed method can replace wheel encoders in odometry estimation or supplement missing GPS data, when the GNSS signal absents (e.g. during the indoor mapping). Our solution brings real-time performance and precision which are useful to provide online preview of the mapping results and verification of the map completeness in real time

A high-precision liDAR-based method for surveying and classifying coastal notches

Formation of notches is an important process in the erosion of seaside cliffs. Monitoring of coastal notch erosion rate and processes has become a prime research focus for many coastal geomorphologists. Observation of notch erosion rate considers a number of characteristics, including cliff collapse risk, distinction of historical sea levels, and recognition of ongoing erosional mechanisms. This study presents new approaches for surveying and classifying marine notches based on a high-precision light detection and ranging (LiDAR)-based experiment performed on a small region of a coastal cliff in southern Portugal. A terrestrial LiDAR scanner was used to measure geometrical parameters and surface roughness of selected notches, enabling their classification according to shape and origin. The implemented methodology proved to be a highly effective tool for providing an unbiased analysis of marine morphodynamic processes acting on the seaside cliffs. In the analyzed population of voids carved into Miocene calcarenites in a coastal cliff section, two types of notch morphology were distinguished, namely U-shaped and V-shaped. The method presented here provides valuable data for landscape evaluation, sea-level changes, and any other types of analyses that rely on the accurate interpretation of cliff morphological features.National Science Centre [UMO-2015/17/D/ST10/02191