Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of
LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for
DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage
and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity
characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented.

/pdf/airborne-lidar-for-dem-generation-some-critical-issues-sxb3b6yug9.pdf

Airborne LiDAR for DEM generation: some critical issues

Unmanned aerial vehicles have become popular platforms for remote-sensing applications, particularly when spaceborne technology, manned airborne techniques, and in situ methods are not as efficient...

Recent applications of unmanned aerial imagery in natural resource management

. Small off-the-shelf unmanned aircraft systems (UAS) could prove useful for surveying waterbirds. A low-end model was evaluated for surveying flocks of Canada Geese (Branta canadensis) and Snow Geese (Chen caerulescens) by comparing photographic counts from repeated flybys to repeated visual ground counts. Due to low contrast of Canada Geese with the ground, UAS counts based on confident detections only had a lower mean than ground counts for five out of six flocks (>30% lower for three flocks) and coefficients of variation (CV) ranging from 11–106%, compared to 1–6% for ground counts. Conversely, UAS counts of high-contrast Snow Geese were 60% higher on average and less variable (CV = 1–6%) than ground counts (CV = 11%). In some cases the aircraft likely detected birds that were not seen from the ground due to an obstructed view. Shortcomings of the UAS were mainly related to its unsophisticated imaging system compared to more expensive models. Otherwise, the UAS proved capable of being convenie...

https://bioone.org/journals/waterbirds/volume-35/issue-1/063.035.0119/Evaluation-of-an-off-the-shelf-Unmanned-Aircraft-System-for/10.1675/063.035.0119.pdf

Evaluation of an off-the-shelf Unmanned Aircraft System for Surveying Flocks of Geese

The purpose of this study is to examine the use of multi-resolution object-based classification methods for the classification of Unmanned Aircraft Systems (UAS) images of wetland vegetation and to compare its performance with pixel-based classification approaches. Three types of classifiers (Support Vector Machine, Artificial Neural Network and Maximum Likelihood) were utilized to classify the object-based images, the original 8-cm UAS images and the down-sampled (30 cm) version of the image. The results of the object-based and two pixel-based classifications were evaluated and compared. Object-based classification produced higher accuracy than pixel-based classifications if the same type of classifier is used. Our results also showed that under the same classification scheme (i.e. object or pixel), the Support Vector Machine classifier performed slightly better than Artificial Neural Network, which often yielded better results than Maximum Likelihood. With an overall accuracy of 70.78%, object-b...

https://tandfonline.com/doi/pdf/10.1080/22797254.2017.1373602

Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery

A major problem an instructor experiences is the systematic monitoring of students’ academic progress in a course. The moment the students, with unsatisfactory academic progress, are identified the instructor can take measures to offer additional support to the struggling students. The fact is that the modern-day educational institutes tend to collect enormous amount of data concerning their students from various sources, however, the institutes are craving novel procedures to utilize the data to magnify their prestige and improve the education quality. This research evaluates the effectiveness of machine learning algorithms to monitor students’ academic progress and informs the instructor about the students at the risk of ending up with unsatisfactory result in a course. In addition, the prediction model is transformed into a clear shape to make it easy for the instructor to prepare the necessary precautionary procedures. We developed a set of prediction models with distinct machine learning algorithms. Decision tree triumph over other models and thus is further transformed into easily explicable format. The final output of the research turns into a set of supportive measures to carefully monitor students’ performance from the very start of the course and a set of preventive measures to offer additional attention to the struggling students.

/pdf/an-artificial-intelligence-approach-to-monitor-student-4rna4bbclx.pdf

An Artificial Intelligence Approach to Monitor Student Performance and Devise Preventive Measures.

Surveying and land information science

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Surveying and land information science