Maize is a globally important staple crop, and automated monitoring of germination and seedling emergence is essential for precision agriculture, enabling timely reseeding and reducing potential yield loss. To address this need, we propose Seedling-DETR, a transformer-based model for the real-time detection of emerged and missing maize seedlings using multispectral UAV imagery in an end-to-end manner. First, we construct a multispectral UAV dataset and introduce a dedicated annotation strategy in which missing seedlings were labeled individually rather than inferred indirectly. Then, we modify the feature fusion module of RT-DETR and develop a hybrid-scale feature fusion module to obtain richer and more expressive feature representations for missing seedling detection and improve the precision of missing seedling detection. Finally, we propose a channel fusion module to incorporate multispectral images into our model without requiring a dedicated multispectral backbone or additional pretraining, thereby improving model adaptability. The results show that, under a random train–test split (8:2), when using RGB images as input, our Seedling-DETR achieves a mean average precision (mAP) of 83.1% at an IoU threshold of 0.5, outperforming YOLOv11x and RT-DETR by 2.5% and 1.1%, respectively. The proposed method achieves an AP of 69.3% at an IoU threshold of 0.5 for missing seedling detection, which increases to 71.7% when multispectral inputs are incorporated. Similar performance trends are observed on an independent validation set collected on a different date. Although the model introduces moderate computational overhead (282 GFLOPs for RGB input and 418 GFLOPs for multispectral configuration, with 84.0 M and 85.1 M parameters, respectively), it can maintain efficient detection performance suitable for actual agricultural field deployment. The method is further validated at the field scale using orthomosaic-based analysis. Overall, this study provides an effective and scalable framework for the detection of emerged and missing maize seedlings under complex field conditions. The proposed framework supports accurate reseeding decisions, and contributes to automated maize production.

Ulaanbaatar, Mongolia, relies heavily on coal for winter heating, and continued urban expansion is expected to increase heating demand. This study investigated heating demand and rooftop PV potential in Ulaanbaatar from 2016 to 2025 using Google Earth Engine. Nighttime light intensity, heating degree days (HDD), and solar radiation datasets were integrated to evaluate changes in human activity, climate-driven heating demand, and rooftop solar resources. Electricity-equivalent heating demand, rooftop PV generation, coal displacement potential, and techno-economic performance under different policy scenarios were assessed. The results reveal a clear increase in urban built-up area during the study period, accompanied by rising nighttime light intensity, indicating growing urban activity. Despite relatively stable HDD, urban expansion implies increasing potential heating demands. Under the main rooftop PV deployment scenario, surplus electricity could theoretically offset approximately 10.9% of coal-based heating demand and reduce CO₂ emissions by 1.126 MtCO₂ yr⁻¹ through resistance heating. When the same surplus was converted through heat pumps with COP = 1.86, the theoretical coal-heating offset increased to 20.3%. However, economically viable large-scale deployment remained strongly dependent on feed-in tariff support. These findings indicate that continued urban expansion may lead to higher heating demand in Ulaanbaatar and highlight both the opportunities and structural limitations of rooftop PV for decarbonizing coal-dependent heating systems in cold-climate cities.