Crop biomass, a critical indicator of plant growth, health, and productivity,
is invaluable for crop breeding programs and agronomic research. However, the
accurate and scalable quantification of crop biomass remains inaccessible due
to limitations in existing measurement methods. One of the obstacles impeding
the advancement of current crop biomass prediction methodologies is the
scarcity of publicly available datasets. Addressing this gap, we introduce a
new dataset in this domain, i.e. Multi-modality dataset for crop biomass
estimation (MMCBE). Comprising 216 sets of multi-view drone images, coupled
with LiDAR point clouds, and hand-labelled ground truth, MMCBE represents the
first multi-modality one in the field. This dataset aims to establish benchmark
methods for crop biomass quantification and foster the development of
vision-based approaches. We have rigorously evaluated state-of-the-art crop
biomass estimation methods using MMCBE and ventured into additional potential
applications, such as 3D crop reconstruction from drone imagery and novel-view
rendering. With this publication, we are making our comprehensive dataset
available to the broader community.

通过多视角无人机影像和 LiDAR 点云相结合的数据集，本研究提出了第一个多模态农作物生物量估计数据集，并通过评估现有方法的性能和探索新的潜在应用，如无人机图像下的 3D 农作物重建和新视角渲染，为农作物生物量量化的基准方法和视觉方法的发展提供了支持。

MMCBE：用于作物生物量估计和更多领域的多模态数据集

MMCBE: Multi-modality Dataset for Crop Biomass Estimation and Beyond

In modern battlefield scenarios, the reliance on GPS for navigation can be a
critical vulnerability. Adversaries often employ tactics to deny or deceive GPS
signals, necessitating alternative methods for the localization and navigation
of mobile troops. Range-free localization methods such as DV-HOP rely on
radio-based anchors and their average hop distance which suffers from accuracy
and stability in a dynamic and sparse network topology. Vision-based approaches
like SLAM and Visual Odometry use sensor fusion techniques for map generation
and pose estimation that are more sophisticated and computationally expensive.
This paper proposes a novel framework that integrates landmark-based
localization (LanBLoc) with an Extended Kalman Filter (EKF) to predict the
future state of moving entities along the battlefield. Our framework utilizes
safe trajectory information generated by the troop control center by
considering identifiable landmarks and pre-defined hazard maps. It performs
point inclusion tests on the convex hull of the trajectory segments to ensure
the safety and survivability of a moving entity and determines the next point
forward decisions. We present a simulated battlefield scenario for two
different approaches (with EKF and without EKF) that guide a moving entity
through an obstacle and hazard-free path. Using the proposed method, we
observed a percent error of 6.51% lengthwise in safe trajectory estimation with
an Average Displacement Error (ADE) of 2.97m and a Final Displacement Error
(FDE) of 3.27m. The results demonstrate that our approach not only ensures the
safety of the mobile units by keeping them within the secure trajectory but
also enhances operational effectiveness by adapting to the evolving threat
landscape.

在现代战场情景中，全球定位系统（GPS）的依赖性可能存在重大的漏洞。该研究提出了一种新的框架，将基于地标的定位与扩展卡尔曼滤波器（EKF）相结合，以预测沿战场移动实体的未来状态。结果表明，该方法不仅能确保移动单位的安全性，使其保持在安全轨迹上，而且能够适应不断演化的威胁环境，提高作战效能。