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Remote Sensing Applications Consultants Limited

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Remote Sensing Applications Consultants Limited is a UK company with status active founded in 1996 based in South East England.

CRN
03158371
Founded
1996
Age
30

Overview

Legal name
REMOTE SENSING APPLICATIONS CONSULTANTS LIMITED
Region
South East England
Registered address
LITTLE BARN SUTTON MANOR
BISHOP'S SUTTON
ALRESFORD
HAMPSHIRE
ENGLAND
SO24 0AA
Insolvency history
No

Company events

Reference milestones and recent Companies House filing stream events.

8 events
28 Feb
2027

Confirmation statement due

Confirmation Due

Next confirmation statement due date

31 Dec
2026

Accounts due

Accounts Due

Next accounts due date

14 Feb
2026

Confirmation statement filed

Confirmation

Last confirmation statement made up date

31 Mar
2025

Accounts filed

Accounts

Last accounts made up date

30 Oct
2024

Accounts With Accounts Type Total Exemption Full

Accounts Analysed

AA | Transaction MzQ0MTQ2NzY3OGFkaXF6a2N4

Published 30 Oct 2024 03:10

19 Sep
2024

Change To A Person With Significant Control

Persons-with-significant-control

PSC04 | Transaction MzQzNjUyODYyNmFkaXF6a2N4

Published 19 Sep 2024 12:17

19 Sep
2024

Termination Director Company With Name Termination Date

Officers

TM01 | Transaction MzQzNjUyNzY4N2FkaXF6a2N4

Published 19 Sep 2024 12:08

14 Feb
1996

Incorporated

Inception

Company registered at Companies House

Public funding

7 awards
First funded
2013
Funded years
2013, 2014, 2015, 2016, 2017, 2019
Age at first award
17 years

Projects

2019 BIS-Funded Programmes Lead participant

SatCafe - Satellite Remote Sensing for Improved and Sustainable Coffee Production

1 Feb 2019 to 31 Jul 2021

Awarded
£149,225
Total cost £218,389

Awaiting Public Project Summary

2017 Feasibility Studies Lead participant

Sentinel-1 Crop Growth Monitor

1 Jun 2017 to 31 May 2018

Awarded
£43,570
Total cost £96,824

Information on crop growth is an important metric allowing analysis of agricultural productivity, prediction of yield and prescription of appropriate interventions. This project will develop a service based on crop growth indices derived from time series of satellite radar data, emerging technology that was initially developed by RSAC in the MASCOTS proje...

2016 Feasibility Studies

SHiFT: ¬Sentinel-2 -compatible Historical datasets for Future crop Targeting

1 Oct 2016 to 30 Sep 2017

Awarded
£29,390
Total cost £41,998

The project will investigate the use of archive satellite imagery to predict spatial variability within arable fields. Many applications of precision agriculture use current satellite imagery to provide guidance on localised management operations, for example application of Nitrogen fertiliser, but assumptions have to be made about the causes of spatial v...

2015 EU-Funded Lead participant

Eurostars MASCOTS

1 Apr 2015 to 30 Sep 2017

Awarded
£144,710
Total cost £241,183

Awaiting Public Project Summary

2014 Collaborative R&D Lead participant

Crowdsourcing Landscape Change

1 Sep 2014 to 31 May 2016

Awarded
£122,824
Total cost £204,706

Local authorities such as Hampshire County Council (HCC) have huge archives of aerial photography and satellite image data that contain an abundance of information relating to changes in the landscape. However, because of the volume and complexity of the data and the lack of resources and political will, their value is not being fully realised. Crowdsourc...

2014 Feasibility Studies Lead participant

Land Cover Plus: national agricultural land cover information for the water industry

1 Apr 2014 to 31 Mar 2015

Awarded
£55,634
Total cost £74,178

Anglian Water needs detailed, up to date and consistent information on cropping within its drinking water catchment to better understand catchment risks and to target mitigation measures with greater accuracy and efficiency. The forthcoming Copernicus Sentinel-1 mission is designed specifically to service operational applications, including agriculture, w...

2013 Launchpad

CropID Prototype

1 Sep 2013 to 31 May 2015

Awarded
£10,994
Total cost £24,430

The CropID system will classify horticultural crops using a machine learning approach integrating; multi-spectral satellite imagery, synthetic aperture radar data, soil properties, physical field characteristics. Image processing algorithms will be used to segment crops based on spectral reflectance, colour and texture. Satellite images acquired through t...

Product types

BIS-Funded Programmes Collaborative R&D EU-Funded Feasibility Studies Launchpad