This issue of the Cornerstone Report is the USIBD’s second report on Standards in the Building Documentation industry. In particular, this issue looks at the adoption of standards. It has been a couple of years since the USIBD last looked at standards, so they are in a unique position to see how the industry is developing.
The report offers a comparison between the 2017 survey responses and those received in 2015. Questions include are stakeholders aware of standards, how are standards being established and do they use industry standards or create their own?
The adoption of industry standards is in the best interest of all concerned.
The Democratic Republic of Congo has become the first African country to build a national biomass map using LiDAR, furthering the scientific underpinning of their REDD+ processes.
WWF-DRC launched the Carbon Map and Model project in 2012, in partnership with the Ministry of Environment of the DRC and WWF-Germany, and with the support of its partners, University of California, Los Angeles, Southern Mapping Company, and Observatoire Satellital des Forêts d’Afrique Centrale. This project was developed to complete the national forest biomass map through an aerial LiDAR sampling approach.
Each LiDAR sampling site was selected systematically and randomly throughout DRC, incorporating a variety of plots which represent the various Congolese rainforest types. In total, the flight campaign covered 216 sites and over 430,000 ha. All available plot data were compiled and processed to develop a LiDAR biomass model, which was then extrapolated to national scale by satellite imagery, including optical Landsat 8 and active radar data.
The map will support national forest cover monitoring efforts, which include identification of deforested and degraded areas, as well as helping to assess annual carbon emissions from deforestation, and the necessary reporting to the international mechanisms for REDD+. The biomass information from the map has already been integrated into the final Maï-Ndombe Emissions Reduction Program, which is the largest REDD+ program in Africa.
This carbon map will strengthen REDD+ in DRC, providing additional layers of data on forest cover changes and emissions. With tropical deforestation making up nearly 10 percent of global carbon emissions, the success of REDD+ in DRC, where over 60% of Central Africa’s forest is located, is essential to efforts to mitigate global climate change.
The use of multispectral cameras to remotely sense land cover over wide areas has been used for a number of years. More recently the use of multiple wavelength lidar sensors are being studied with the hope of more accurately classifying land cover types.
Researchers at Ryerson University in Canada are reporting impressive results from their use of multispectral lidar sensors that are capable of recording a diversity of spectral reflectance from objects.
They used two different methods to develop land cover classification of an urban study area. The first was image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied.
The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass.
An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively.
As background Waymo, the self-driving car company owned by Google parent company Alphabet, sued Uber in February for patent infringement and theft of trade secrets. Waymo claims that its former employee Levandowski downloaded 14,000 confidential files before leaving to launch the self-driving truck company Otto, which was acquired by Uber. Waymo argues that 14,000 documents are being used to develop Uber’s self-driving technology, while Uber contends that the files never made it onto its servers.
In preparation for a preliminary injunction hearing next month, Waymo filed new claims last week, alleging that Uber withheld information about technology they are using in its development of self-driving cars. Waymo claims Anthony Levandowski, a former Google employee, stole confidential material and used it to jumpstart Uber’s self-driving program.
Uber claimes that its LiDAR system isn’t ready and therefore isn’t being used in its self-driving cars, which rely instead on commercial systems furnished by Velodyne. Waymo now says that Uber worked on a second system that more closely copies Waymo’s designs.
Waymo also claims Uber participated in a “cover up” to keep its second, un-named design hidden from the court. “They were hiding a device,” Waymo’s lawyers said.
Velodyne LiDAR Inc., a world leader is 3D real-time perception systems for autonomous vehicles, announced its new fixed-laser, solid-state Velarray™ LiDAR sensor, a cost-effective yet high-performance and rugged automotive product in a small form factor. With the Velarray sensor, which can be seamlessly embedded in both autonomous vehicles and advanced driver-assist safety (ADAS) systems, Velodyne LiDAR again sets the industry standard for image quality and functionality delivered in smaller, more cost-effective form factors.
“The Velarray enables not only fully autonomous vehicles, but also ADAS systems such as adaptive cruise control, while at the same time providing a miniature form factor and mass production target prices,” said Mike Jellen, President & Chief Commercial Officer, Velodyne LiDAR. “It offers a unique value proposition empowering a vehicle system that improves the safe driving experience, alongside an upgraded path to full autonomy.”
The new Velarray LiDAR sensor uses Velodyne’s proprietary ASICs (Application Specific Integrated Circuits) to achieve superior performance metrics in a small package size of 125mm x 50mm x 55mm that can be embedded into the front, sides, and corners of vehicles. It provides up to a 120-degree horizontal and 35-degree vertical field-of-view, with a 200-meter range even for low-reflectivity objects. With an automotive integrity safety level rating of ASIL B, Velarray will not only ensure safe operation in L4 and L5 autonomous vehicles but also in ADAS-enabled cars. It has a target price in the hundreds of dollars when produced in mass volumes.
Velodyne has a lot riding on this new technology.
The following is intended to correct an error in a previous post on this topic. I am sorry for the confusion.
” AGERpoint, Inc.™ is proud to announce the development of its new LiDAR sensor, the GML100. The GML100 is the first Geiger-mode avalanche photodiode LiDAR unit on the market to offer multiple returns per flash per detector and a form factor that can be used across a wide array of industries and mobile settings. The increase in point cloud density made possible by this innovation results in higher quality three-dimensional point cloud data compared any product in its price category currently on the market.”
AGERpoint is only claiming that they have developed the first GmAPD Lidar capable of multiple returns per flash by reducing the impact of “blanking loss” which has been erroneously been assumed to be an inherent characteristic of all GmAPD devices.
Certainty 3D’s 2nd TopoDOT User Conference is just 3 short weeks away. You don’t have to be a subscriber to attend this outstanding learning and networking event. TUC 2017 is being held at a local community college which includes a number of well equipped computer labs as well as class rooms that support both the technical and the management tracks.
If you need to extract features or topography and create CAD objects in Microstation then you should take a look at TopoDOT.
The conference runs from May 8 – 12 in Orlando, Florida. Last year’s TUC was a high value event. I can’t wait to see what Ted and his team have planned for this year.
Traditional methods for segmenting trees attempt to isolate prominent tree crowns from a lidar-derived canopy height model. This group of researchers have introduced a novel segmentation method, “layer stacking,” which slices the entire forest point cloud at 1-m height intervals and isolates trees in each layer. Merging the results from all layers produces representative tree profiles.
When compared to watershed delineation (a widely used segmentation algorithm), layer stacking correctly identified 15% more trees in unevenaged conifer stands, 7%–17% more in even-aged conifer stands, 26% more in mixedwood stands, and 26%–30% more (with 75% of trees correctly detected) in pure deciduous stands.
Overall, layer stacking’s commission error was mostly similar to or better than that of watershed delineation. Layer stacking performed particularly well in deciduous, leaf-off conditions, even those where tree crowns were less prominent. We conclude that in the tested forest types, layer stacking represents an improvement in segmentation when compared to existing algorithms.
Treesearch is an online system for sharing free, full text publications by Research and Development scientists in the US Forest Service. Included in Treesearch are scholarly works published by the agency as well as those published by others, including papers appearing in journals, conference proceedings, or books. All publications appearing in Treesearch are based on peer reviewed research to make sure they provide the best scientific information possible.
The Treesearch website has been delivering publications since January 2004, starting with a collection of just 7,000 and passing 40,000 by its tenth anniversary, with thousands added each year. A new version of Treesearch was launched in December 2013; see the bottom of this page for changes.
Searches in Treesearch are focused on the metadata surrounding a publication, things like author, title, and so forth. An alternative way to search the same collection is GeoTreesearch, an interactive map-based tool that combines full text searches of the publications themselves with a geographic filter based on the places mentioned in that text.
AGERpoint, Inc.™ has developed the first Geiger-mode avalanche photodiode LiDAR. The GML100 unit offers multiple returns per flash per detector and a form factor that can be used across a wide array of industries and mobile settings.
The acquisition software of the GML100 permits real-time point cloud viewing, facilitating mission planning and monitoring. Taken together, the mobility and efficiency of the GML100 make it an ideal candidate for deployment in agriculture, defense, and forestry, as well as infrastructure projects such as highway, rail, and utility construction.
Data from the GML100 can be output in industry standard LAS and LAZ formats for analysis, or downloaded to AGERpoint for analysis and reporting through the AGERpoint web-based data platform. Agricultural growers also have the option of integrating their data with the AGERmetrix agricultural information management system.
With pricing projected to be more than 50% below those of sensors currently available, AGERpoint’s entry into the LiDAR sensor market promises to bring unprecedented levels of accuracy to the 3D modeling landscape in a highly mobile, cost-effective package.