The processes of mobilization of land for infrastructures of public and private domain are developed according to proper legal frameworks and systematically confronted with the impoverished national situation as regards the cadastral identification and regularization, which leads to big inefficiencies, sometimes with very negative impact to the overall effectiveness.
This project report describes Ferbritas Cadastre Information System (FBSIC) project and tools, which in conjunction with other applications, allow managing the entire life-cycle of Land Acquisition and Cadastre, including support to field activities with the integration of information collected in the field, the development of multi-criteria analysis information, monitoring all information in the exploration stage, and the automated generation of outputs. The benefits are evident at the level of operational efficiency, including tools that enable process integration and standardization of procedures, facilitate analysis and quality control and maximize performance in the acquisition, maintenance and management of registration information and expropriation (expropriation projects). Therefore, the implemented system achieves levels of robustness, comprehensiveness, openness, scalability and reliability suitable for a structural platform.
The resultant solution, FBSIC, is a fit-for-purpose cadastre information system rooted in the field of railway infrastructures.
FBSIC integrating nature of allows: to accomplish present needs and scale to meet future services; to collect, maintain, manage and share all information in one common platform, and transform it into knowledge; to relate with other platforms; to increase accuracy and productivity of business processes related with land property management.
Fernando Gil's insight:
Fernando Gil's master thesis: "The implementation of an Enterprise Geographical Information System to support Cadastre and Expropriation activities." published at ISEGI/NOVA digital library site (http://hdl.handle.net/10362/13786)
Canada: PCI Geomatics has released a new UAV image alignment and analysis tool, STAX. The tool provides aligning and analysing UAV imagery without the steep learning curve or price of a full photogrammetric software suite. STAX was built specifically to address the challenges of collecting and aligning multiple UAV surveys of the same location over …
Pipeline operators are responsible for the safe transport of oil and gas through high-pressure transmission pipelines. In the Western world, these transmission pipelines are buried in the public space at a depth of about 1.5 metres. Operators monitor the integrity of their pipelines on a regular or even continuous basis, as pipeline failures can cause severe damage to people, infrastructure and the natural and built environment. Read on for a discussion of the use of Copernicus Sentinel-1 satellite radar imagery to provide pipeline operators with a continuous source of information for monitoring and managing their assets from space.
When you go from a 5-meter cell size to a 10-meter cell size, cell size will be different in the output raster grid. When converting raster data between different coordinate systems, cell centers don’t match. In both situations, a resampling approach must be taken to specify how the output grid will take shape.
But it’s not always an easy choice which resampling method to use because there’s more than one way to recalculate cell values. There are four common ways to resample raster grids in GIS.
Una de las cuestiones más recurrentes que nos encontramos a la hora de trabajar con PostgreSQL y PostGIS en entornos de producción, es la de cómo acceder a la base de datos desde programas externos. Es decir, cómo, por ejemplo, consultar la base de datos o actualizarla a través de Python.
De nuevo ha sido uno de los alumnos de nuestro Master GIS con Python el que lo ha resuelto. Dentro del material de formación tenemos un módulo que habla de conexiones con bases de datos pero no se hablaba, hasta ahora, de esta conexión.
Human activities are leaving their fingerprints across Earth (Figure 1), driven by increasing populations, technological capacities, and societal demands [e.g., Ellis, 2015; Brown et al., 2017; Waters et al., 2016]. We have altered flood patterns, created barriers to runoff and erosion, funneled sedimentation into specific areas, flattened mountains, piled hills, dredged land from the sea, and even triggered seismic activity [Tarolli and Sofia, 2016]. These and other changes can pose broad threats to the sustainability of human societies and environments.
San Francisco is experiencing some well-publicized growing pains. Employment in the city is on the rise, housing costs are going up, and longtime residents are being displaced. Shuttle bus services for big companies are under scrutiny because they have brought an influx of new residents and a subsequent spike of housing prices (and evictions) along their routes.
For a city that's bounded by water on three sides, the question at hand is, "How can growth be managed smartly within a finite area and a transition to infill development with the corresponding lack of easily developed vacant land?"
Dà-Jiāng Innovations Science and Technology Co., Ltd is a manufacturer of unmanned aerial vehicles, or drones, based in Shenzhen, China. By most accounts, DJI is a dominant player in the marketplace with an estimated market share of about 75 percent. Their product line includes the prosumer Phantom 3 and 4 series and the newly announced Mavic. The Inspire series and the Matrice round out their professional UAV line. DJI also manufactures controllers, gimbals, and cameras.
I sat down with Michael Perry, director of strategic partnerships for DJI, at the Consumer Electronics Show in Las Vegas last month. We discussed how DJI’s drones and software are used in GIS and geospatial applications.
Stay One Step Ahead with Powerful Predictive Technology As a staff member of the Office of Inspector General, you are the government's first line of defense against financial crimes within your organization. It is no longer enough to recover stolen funds-you must quickly access, analyze, and act on information to prevent fraud, waste, and abuse.
ArcGIS is a complete platform that allows you to aggregate and visualize data, find the trends, and proactively stop crimes before the money is lost.
Fraud is being perpetuated by increasingly sophisticated technologies, and you need to stay ahead of the curve. With ArcGIS, your auditors and investigators can access data through an open platform and use it to quickly optimize workflows, increase return on investment, and share successes with the public.
A new app called MapSwipe has been created in order to put the world’s most vulnerable populations back on the map using modern technology. MapSwipe and its team of digital volunteers utilize the abundance of smart phone technology to gather more information on natural disasters and the impact they have on people, cities, and countries around the world.
If you’ve read many of the posts here, you know I like cartograms, maps that encode information by distorting the size of geographic regions.
The last few years have seen an explosion of digital mapping on the web, but for some reason the cartogram has been largely left out. And it’s too bad because in many cases they are a far superior way of representing information than maps that use only color.
Access to consistent high-quality images to study changes on Earth’s surface is getting easier. The USGS Landsat standard (Level-1) product inventory is now structured by data quality and offers improved calibration. Data designated as Tier 1 provide the highest accuracy and can be reliably used to analyze changes to Earth’s surface over time.
Cambodia Experiences Rapid Rate of Forest Loss When it comes to forest loss on a global scale, Cambodia is notable for how rapidly its forests are being cleared. Among countries with accelerated rates of deforestation—Sierra Leone, Madagascar, Uruguay, and Paraguay among them—Cambodia ranks above them all with an annual loss of 14.4 percent of its forests between 2001 and 2014, according to researchers at the University of Maryland, who used Landsat data to track their rates. In that time period, Cambodia lost 5,560 square miles of forests. That loss is easily seen in these Landsat images. The Landsat 5 image captured in 1999 (left) shows vast dark green forest among a mountainous area of Cambodia. The 2017 image acquired by Landsat 8 (right) reveals areas where forest has been clear-cut. The bright green landscapes in the lower left interspersed with darker blocks are crops. The pinkish-tan areas are old, small-plot agricultural areas, and the bright green rectangles (top left) are agroforestry areas where rubber or oil palm plantations have emerged.
When we were getting SignifAI off the ground, one of the biggest decisions we had to make right at the beginning was what our stack would be. We had to make a typical choice: use a relatively new language like Go or an old, solid one like Python. There are many good reasons for choosing Go, especially when looking for high performance, but because we already had significant experience with Python, we decided to continue with it. But it’s important to note that our product and infrastructure must support hundreds of thousands of events per second. As we are collecting massive amount of events and data points from multiple sources for our customers. Each event should be processed in real time as fast as possible, so we do care about latency as well. It’s not trivial, especially as we need to minimize our compute costs and be as efficient as possible.
A continuación os dejamos todos los módulos de nuestro vídeo curso gratuito de introducción a Python junto con las presentaciones en formato ppt. En cada vídeo se os propone un ejercicio muy sencillo. Recordad que si os registráis al curso tenéis un descuento del 15 % para nuestro Master GIS con Python. Ánimo con el video curso que es bastante asequible y ameno
Efficient and timely post-emergence weed control based on herbicide use is a crucial task in crop production because a wrong early weed management not only reduces the crop yield (due to weed competition) but also increases the negative impacts of herbicides on the environment. Inappropriate weed management is often related with two main problems. The first is applying herbicides when weeds are not in the suitable phenological stage (generally the correct growth stage is when weeds have 4-6 true leaves, although this slightly depends on specific weed species or group of species), the second is broadcasting herbicides over the whole field, even when weed-free areas are present due to the widely demonstrated weed patchy distribution. The first problem is usually addressed using the expert knowledge of farmers. The other problem can be overcome by developing site-specific weed management (SSWM) strategies according to weed emergence. These strategies may consist of both a single herbicide treatment to weed patches where a unique group of weeds is present (for example either grass or broadleaved weeds), or use of several herbicides according to the presence of different weed species or group composition, such as grass, broadleaved weeds or a specific problematic weed (i.e., a herbicide resistant weed).
Data scientists, GIS engineers and software developers from California-based company EOS have recently launched a cutting-edge cloud-based tool that allows users, journalists, researchers and students to easily search and analyze huge amounts of the most up-to-date earth observation data.
"Today, technology has improved alongside more choice and variety of products. There are plenty of web based GIS style interactive maps now displaying the deluge of Earth Observation data (Sentinel2 examples here, or perhaps this Proba-V one) Perhaps this is more of an alien world to the traditional Remote Sensor. Raster data was only previously seen as a base map, you know the thing you had to load in first before doing the 'amazing' GIS 'stuff' (normally vector based, but often to improve performance vector data tiled to rasters :)) and later more advanced geoprocessing. If you are working with Earth Observation data today the easiest way to reach your audience is through the web and web based GIS is still the most common way to do this. First you need to get a web GIS running on your machine locally and this is surprisingly easy to do and should take no more than 15-20mins."
As sources of data for global forest monitoring grow larger, more complex and numerous, data analysis and interpretation become critical bottlenecks for effectively using them to inform land use policy discussions. Here in this paper, we present a method that combines big data analytical tools with Emerging Hot Spot Analysis (ArcGIS) to identify statistically significant spatiotemporal trends of forest loss in Brazil, Indonesia and the Democratic Republic of Congo (DRC) between 2000 and 2014. Results indicate that while the overall rate of forest loss in Brazil declined over the 14-year time period, spatiotemporal patterns of loss shifted, with forest loss significantly diminishing within the Amazonian states of Mato Grosso and Rondônia and intensifying within the cerrado biome. In Indonesia, forest loss intensified in Riau province in Sumatra and in Sukamara and West Kotawaringin regencies in Central Kalimantan. Substantial portions of West Kalimantan became new and statistically significant hot spots of forest loss in the years 2013 and 2014. Similarly, vast areas of DRC emerged as significant new hot spots of forest loss, with intensified loss radiating out from city centers such as Beni and Kisangani. While our results focus on identifying significant trends at the national scale, we also demonstrate the scalability of our approach to smaller or larger regions depending on the area of interest and specific research question involved. When combined with other contextual information, these statistical data models can help isolate the most significant clusters of loss occurring over dynamic forest landscapes and provide more coherent guidance for the allocation of resources for forest monitoring and enforcement efforts.
GEB reader Michael Lee is sharing a free tool he created for importing geotagged photos into Google Earth. It is only available for Windows. It comes in two versions, a standalone version and one with an installer
It is very easy to use. Rather than opening the program directly, you drag and drop a photo, or a folder containing multiple photos onto the program icon or a shortcut to the program. It then creates a KML and opens it in Google Earth. The KML shows camera icons where your photos are. To see a photo when you click on an icon, you first need to save the KML file into the folder where your photos are and reopen it in Google Earth. Note that you cannot drag and drop multiple photos at once, but rather put them into a folder which you can drag and drop.
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