– Verifying Efficiency and Labor-Saving Solutions for Agricultural Committee Inspections Across Two Local Municipalities in Chiba – 

 

We are pleased to announce that Space Shift, in collaboration with Chiba Prefecture (Land and Rural Communities Promotion Division, Department of Agriculture, Forestry and Fisheries), will launch a demonstration project for AI and satellite-driven idle farmland surveys under the prefecture’s newly established initiative, the “Fiscal Year 2026 Model Project for Introducing idle Farmland Remote Sensing.” 

In this project, we will leverage a combination of two satellite types—optical satellites and Synthetic Aperture Radar (SAR) satellites, the latter capable of stable data acquisition regardless of weather conditions. Utilizing our proprietary AI analytics technology, we will detect and visualize idle farmland within Chiba Prefecture’s two model cities: Ichihara City and Narita City. The project aims to demonstrate the efficiency and labor-saving potential of agricultural committee inspections while ultimately supporting efforts to match landowners with prospective cultivators.

Reference: Selection Results for the Fiscal Year 2026 Model Project for Introducing idle Farmland Remote Sensing Outsourcing / Chiba Prefecture [Japanese only] 

■ Background and Objectives 

Against the backdrop of an aging farming population and a shortage of successors, idle farmland has been on the rise in Chiba Prefecture, with the total idle area reaching approximately 8,800 hectares in 2024. Meanwhile, the annual farmland utilization surveys mandated by the Cropland Act rely primarily on visual, on-site inspections, with agricultural committee members and optimization officers patrolling all farmlands on foot. Improving inspection efficiency under constrained staffing and time limits has become an urgent priority. 

Furthermore, because these committee members and optimization officers frequently rotate every two to three years, the assessment process has become highly dependent on individual experience. This reliance on personal judgment and the resulting difficulties in knowledge transfer pose structural challenges on the ground. 

To address these issues, this project combines automated detection of potential idle farmlands using satellite data and AI analytics with an efficient data management support system. By easing the inspection burden, streamlining operations, and visualizing farmland data, the project aims to facilitate matching between landowners and active farmers. Ultimately, this initiative seeks to establish a sustainable farmland management framework, with plans for a broader, region-wide rollout across Chiba Prefecture starting in fiscal year 2027 and beyond. 

■ Project Overview 

• Project Duration: May 2026 – March 2027 
• Target Areas: Two model municipalities selected by Chiba Prefecture (Reference: Total cultivated land area of approx. 12,000 ha)
• Core Technology: SAR and optical satellite data / Proprietary AI analytics (featuring a 10-tier classification for abandonment probability) 

Key Activities 

1. Satellite Monitoring & AI Analytics: Conducting automated idle farmland analysis using optical and SAR satellite data, and delivering the processed datasets. 
2. Target Screening Support: Assisting in narrowing down and prioritizing specific land parcels that require on-site visual inspections based on AI analysis results. 
3. System Integration: Integrating analyzed datasets seamlessly into the Agricultural Committee Support System. 
4. Land Parcel-Linked Visualization Maps: Providing visualization tools tied directly to cadastral map data to support land-matching initiatives. 
5. Evaluation & Rollout Assessment: Compiling demonstration results and evaluating the feasibility of a broader, region-wide rollout. 
6. Training & Educational Support: Cooperating with and supporting training workshops designed to promote the adoption of remote sensing technology. 

Roles and Responsibilities 

• Chiba Prefecture: Overall project management, coordination and communication with model municipalities, and planning/operation of training workshops. 
• Space Shift: Executing satellite AI analytics, providing on-site operational support, and compiling final demonstration results. 

 

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**The information contained in this press release is current as of the date of publication.

 

Media Inquiries
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