Historia sukcesu
Parcel change detection in the Netherlands
Agriculture; crop acreage; Monitor crops; Assess land value, ownership, type, use; Land use; cadastral
Wniosek
The service automatically checks satellite imagery time series and delivers a classification of parcels in line with the requirements. Additionally a dataset of 2D geometries of suspected changes, including properties of the change signal such as nature of change (e.g. building, water, vegetation) and date of change is delivered to add further efficiency to the process of updating the geometries of the parcels, in order to comply with LPIS requirements.
Korzyści
- Savings in hours spent on manual work (and thus on budget)
- Increasing update frequency from 3 years to 1 year (and potentially shorter, depending on availability of satellite imagery).
- Savings due to increased efficiency in the further process of geometry updating based on the change signal properties
Porównanie usług
usługi
powiązana zawartość
Mutation Signalling on agriculture parcels in the Netherlands
Agriculture, crop acreage, Monitor crops, crop types extent, Land use, Assess land value, ownership, type, use, Measure land use statistics, Agricultural commodities, Agriculture and rural development policy, Farming, Real-estate management, Local and regional planners, City authorities, Planners, Regional governments, Town authorities, Agriculture and rural policy makers
NEO signals changes to agriculture parcels. Monitoring, based on satellite imagery in combination with artificial intelligence, aims to detect changes in the boundaries of the parcels. The service saves time and money because, based on the mutation signalling, parcels can be looked at more specifically to determine the new parcel boundaries. This supports the process of keeping the national LPIS (Land Parcel Identification System) registry up to date. The service that NEO provides is unique: it is the first time that mutation signalling based on earth observation is performed automatically on this scale. In this way the 500.000 parcels in the Netherlands are monitored. Open data from the national satellite data portal in the Netherlands is used.
Dodany
Dodany
Solar panel service
Infrastructure, Map and monitor solar energy (solar farms), solar energy, solar radiation, Monitor urban areas, Urban Areas, urban development, urban settlement, urban atlas, urban areas, smart cities, individual houses inventory, Alternative Energy, Regional planners, Solar energy, Environment, Real-estate management
NEO monitors solar panels throughout the Netherlands using deep learning methods. Solar panels are detected using various resolutions of satellite and aerial imagery. The panels are digitalized by the algorithm and related to 3D-roofs based on the AHN (Dutch national height model) to calculate zenith and azimuth angles. This data, combined with data such as solar power, allows NEO to estimate the power output for each object. Solar panels are also provided with parcel and address information and statistical reports, so customers can easily apply the data and relate it to internal registrations.
Dodany
Dodany
Monitoring trees
Forests, Assess Deforestation / Forest Degradation, Assess forest damage due to storms or insects, Monitor forest resources, Land use, Forestry, Forest policy, Forest policy makers, Logging industry, Wood industry
NEO is monitoring trees using satellite data and deep learning methodologies. This service informs people about the status of different trees species in their surrounding/municipality. Trees managed by municipalities can be checked on presence and correct attributes such as tree height.
Dodany
Dodany
EO based phytoplankton biomass service 
Inland Water, Assess and monitor water bodies , Local and regional planners, Regional governments, City authorities, Environment, Utilities (water, electricity, waste) , Water plants, Coastal management agencies
Using knowledge about how certain substances, such as one of the main pigments of the phytoplankton (Chlorophyll a - Chl a) alter the colour of the water, these parameters can be derived from optial Earth Observation (EO) satellite sensors. Water Insight provides monitoring services of Chl a based on EO data which is validated with in situmeasurements. Also the maps of WFD phytoplankton biomass status classes are created, by application of the WFD tresholds on Chl a as a proxy for phytoplankton biomass. The maps are made available in a portal, which allows to follow the lakes in time, zoom and download maps and time series.
Dodany
Dodany
