Under normal circumstances, this means random field inspections, which increase labor costs unnecessarily. One of many tasks, which paying agencies have to face frequently in their agenda is monitoring if farmer declaration of what grows on their fields corresponds to the truth. These topics are however more thoroughly described in our previous posts: EOxCloudless for Common Agricultural Policy and EOxCloudless Sentinel-1 and Sentinel-2 Analysis Ready Data. From 2020 onward crop monitoring with help of remote sensing should be established across Europe, under wings of respective paying agencies (opens new window) as a part of modernization of the CAP ecosystem.

CAP is an economical ecosystem to sustain and support agriculture throughout Europe. It is very actively used to check payments made under the Common Agricultural Policy (opens new window) (CAP) which were approximately 45.5 billion euro (opens new window) in total 2015. In the same way, global data can be styled on the client side in multiple ways on demand while only one set is cached.īut none of that answers the question why we have started to be interested in the Vector Tiles ourselves! # Motivationįirst some pieces of context. Land Parcel Identification System (LPIS) is an IT system based on high resolution satellite images for editing the graphical and descriptive information about agricultural land.

Attribute changes of few selected already loaded features (bringing in a change of styling) can lead to modifying only very little low volume of information on client side, instead of the necessity to reload a whole tile. A big advantage is that styling and raw data are separate in nature. It’s a vector data encoding in the form of binary data tiles, geared towards reduced data size, bandwidth reduction due to caching as much as possible and (potentially) improved rendering performance. There are a lot of resources already available about it, as Vector Tiles have been receiving attention for some years now, so I won’t delve into too much detail. They are the vector data equivalent of image tiles for web mapping, applying the strengths of tiling - developed for caching, scaling and serving map imagery rapidly - to vector data. Vector tiles make huge maps fast while offering full design flexibility. TL DR, Leaflet & VectorGrid (opens new window) Demo app LPvis (opens new window) showing conformity of farmer crop declaration vs trained classification ML results using API-updated static vector tiles can be seen here: (opens new window) # Why vector tiles?įor a swift introduction into what a Vector Tile is, Mapbox’s Vector Tiles (opens new window) (MVT) website states the following description:

Vector map tiles