Dataset opportunity
Kodama — 工业传感器数据集机会
Kodama 持有的中等工业传感器数据集，可用于预测性维护和异常检测。
Score
47.5
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
49%
Action
获取
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market size (indicative estimate)
全球预测性维护市场 = 2025 年为 136.5 亿美元，复合年增长率为 24.30%。
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-10-19
Kodama raises $7M to bring robotics to forestry
therobotreport.com ↗
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
- ✨Signal
RS1 '机器人滑移装载机'和远程操作的技术概述
source ↗
Profile
Dataset profile
Type
工业传感器数据集
Modality
时间序列
Sector
工业
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
部分
Legal
公司所有 — 可授权
Buyer persona
工业人工智能与维护优化供应商
Kodama 持有一个专有的时间序列数据集，该数据集由部署在偏远森林环境中的物理滑移装载机上的 RS1 自主套件生成。数据包括边缘处理的 LiDAR 和摄像头馈送，并辅以 `industrial_data` 和 `iot_data`，使其成为开发重型工业机械高保真度预测性维护模型的丰富直接来源。
全球预测性维护市场是一个重要且快速扩张的领域，2025 年市场价值为136.5 亿美元，预计复合年增长率为 24.30%。虽然由于数据的专有性质以及从偏远地区连接依赖 Starlink，访问权限需经协商，但其稀有性及其对这个高增长市场的直接适用性使其成为 AI 买家独一无二的宝贵资产。⚠ 尽职调查（有价值的数据，可协商访问）：数据通过专有的 RS1 自主套件在物理滑移装载机上生成；主要数据包括来自偏远森林环境的边缘处理的 LiDAR 和摄像头馈送；连接依赖 Starlink，表明数据可能分批或本地存储在机器上 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据共同证明 Kodama 拥有其在活跃作业现场运行的自主伐木车辆——“机器人滑移装载机”生成的专有时间序列数据。这个独特的数据集是开发预测性维护模型的工业人工智能供应商的关键资产，该市场预计到 2025 年将达到 136.5 亿美元。该数据捕获了真实的工业机器人性能，提供了对独特操作和故障条件进行算法训练的难得机会，而这些条件在模拟环境中是无法获得的。
See dimension details ↓- Dataset Specificity90
主导的 'iot_data'，工业领域，3 种特定类型
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 个证据命中
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
实时/流式传输
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
适用于预测性维护
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand95
AI 买家需求异常高，这得益于预测性维护市场的快速增长，该市场正以 24.30% 的复合年增长率扩张。
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility50
受限/未知
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility30
中等难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 种证据类型，3 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License92
所有权=公司所有，许可=干净
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
独立
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation39
1 个数据需求信号（1 种类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高，1 个近期外部信号 — 超出已货币化的专有数据
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit58
⚠ 审查 — Kodama 的核心业务是为重型林业设备销售支持 AI 的软件和机器人服务，使其成为技术供应商，而不是休眠数据持有者。问题：公司的核心产品是用于伐木设备的 AI 软件和远程控制系统，这是一种销售智能的形式；业务模式是向伐木公司销售“滑移服务”和技术，而不是进行伐木本身并产生数据作为副产品；公司明确销售技术和 AI 能力，这是一个排除标准；他们收集的数据用于改进自己的 AI 产品，而不是来自独立运营业务的休眠资产。
- Deep Qualification80
✓ 通过 — Kodama 是一个数据持有者，作为其核心业务（提供远程和自主伐木设备即服务）的副产品生成专有传感器数据。虽然数据与机会一致，但由于没有公开的客户协议，转售的许可权未确定。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
这些证据表明收集了来自公司专有“机器人滑移装载机”的时间序列传感器数据，提供了现场工业机器人运行指标的直接馈送。
Geospatial data
这些证据指向由激光雷达和摄像头捕获的表格环境数据，可用于将机器性能与特定地形和操作条件相关联。
Industrial data
这些证据证实了数据集是在活跃作业现场的商业运营期间生成的，确保了数据反映了真实的工业磨损情况，可用于构建强大的预测性维护模型。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Kodama Industrial Sensor — a Moderate industrial sensor dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market = $13.65B in 2025, CAGR 24.30% (source: Fortune Business Insights). Investment score 47.5/100 (confidence 0.49). Recommended action: Acquire.
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