Heavy-polluting enterprises burn fossil fuels to release large amounts of greenhouse gases, causing severe pollution worldwide. Heavy-polluting enterprises have a significant responsibility for carbon emissions, and more than 130 countries have set or are considering targets for achieving net-zero carbon emissions by 2050. Assessing these enterprises can provide data support for carbon emissions and aid in evaluating industry’s economic development. In view of the problem that the existing research data is not comprehensive and the generalisation ability is week. To address this issue, we construct a high-resolution remote sensing image dataset of global heavy-polluting enterprises and use the classic target detection network SSD, Faster R-CNN and YOLOv3 for training, testing and evaluation. The experimental results findings indicate that the SSD network is particularly well-suited for object detection of heavy-polluting enterprises in the remote sensing domain.