在全球南方快速扩张的特大城市中，严重的数据匮乏悖论常阻碍有效的环境监测和循证城市规划。尽管严重的交通拥堵导致局部急性污染，但由于完全缺乏连续的城市交通计数器、普遍的车辆遥测数据以及空气质量监测网络，这些环境在实证层面处于未监测状态。本研究通过引入一种可复现的地理计算框架来解决这一挑战，该框架旨在通过融合多源地理空间数据和深度学习工作流，绘制微观尺度的交通活动和相对排放景观。以刚果民主共和国金沙萨这一未受监测的环境为重点，我们的方法利用分辨率高达 15 cm 的卫星影像训练并部署本地化的 YOLOv8（You Only Look Once, version 8）计算机视觉模型，建立车队分布的实证基线。这些静态计数通过概率性 Hub and Spoke Gravity Model（中心-辐射重力模型）动态分布在城市路网中，该模型权重通过对非结构化 OpenStreetMap 要素进行零样本 Large Language Model（大语言模型）语义重分类来确定。随后，重构的运动学流通过依赖于速度的 Tier 3 EMEP/EEA 算法进行处理，以捕捉由严重网络僵局触发的非线性排放惩罚。为确保框架的可辩护性，我们在十个不同的城市地点执行了独立的无人机观测活动。无人机数据直接验证了模型估计的物理参数，在交通量（R² = 0.826）和路段速度（R² = 0.925）方面实现了稳健的相关性。由于文献衍生的化学排放因子无法在现场验证，所有绝对输出均经过严格的多参数敏感性分析，仅以有界范围报告发现。结果揭示了城市空间活动特征中明显的结构性交接。日间污染主要由非正规辅助公共交通车队驱动，尽管其在活跃车辆中数量占少数，但在主要走廊上贡献了总 PM2.5 排放量的 82.8% 至 95.1% 的有界范围。相反，夜间基线排放转变为几乎完全由私人乘用车主导的景观。所有代码、处理管道和验证数据集均在符合 FAIR 原则的开放获取存储库中提供。该框架为诊断全球未监测大都市区域的交通活动结构建立了可扩展的、遥感驱动的蓝图。

In rapidly expanding megacities across the Global South, a profound data scarcity paradox frequently prevents effective environmental monitoring and evidence driven urban planning. While severe traffic congestion generates acute localised pollution, the total absence of continuous municipal traffic counters, ubiquitous vehicle telemetry, and air quality monitoring networks leaves these environments empirically unmonitored. This study resolves this challenge by introducing a reproducible geocomputational framework designed to map microscale traffic activity and relative emission landscapes by fusing multisource geospatial data and deep learning workflows. Focusing on the unmonitored context of Kinshasa, Democratic Republic of the Congo, our methodology uses very high resolution 15 cm satellite imagery to train and deploy a localised YOLOv8 (You Only Look Once, version 8) computer vision model, establishing an empirical baseline of fleet distribution. These static counts are dynamically distributed across the urban road network using a probabilistic Hub and Spoke Gravity Model, which is weighted through a zero shot Large Language Model semantic reclassification of unstructured OpenStreetMap features. Reconstructed kinematic flows are subsequently processed through velocity dependent Tier 3 EMEP/EEA algorithms to capture the nonlinear emission penalties triggered by severe network gridlock. To ensure framework defensibility, an independent unmanned aerial vehicle observation campaign was executed across ten distinct urban sites. The drone data directly validate model estimated physical parameters, achieving robust correlations for traffic volumes (R2 = 0.826) and link speeds (R2 = 0.925). Because literature derived chemical emission factors cannot be validated in situ, all absolute outputs are subjected to a rigorous multiparameter sensitivity analysis to report findings strictly as bounded ranges. The results reveal a clear structural handover in the spatial activity profile of the city. Daytime pollution is heavily driven by the informal paratransit fleet, which accounts for a bounded range of 82.8% to 95.1% of total PM2.5 emissions along primary corridors despite representing a numerical minority of active vehicles. Conversely, nocturnal baseline emissions transition to a landscape dominated almost entirely by private passenger vehicles. All code, processing pipelines, and validation datasets are available in a FAIR compliant open access repository. This framework establishes a scalable, remote sensing driven blueprint for diagnosing traffic activity structures in unmonitored metropolitan regions worldwide.