We propose a pioneering approach for gathering data on the forest canopy, one that merges two cutting-edge technologies: ConvNext tiny and simple linear iterative clustering (SLIC) (ConvNeXt and SLIC vectorize for tree species mapping, CSVTSM). By leveraging the clustering label generated by SLIC, CSVTSM obtains the vectorized result of land cover and allows us to obtain the location and distribution range information about the individual canopy. Moreover, CSVTSM employed ConvNeXt tiny, an advanced model, to identify vector objects and produce vector tree species maps across the experimental area to obtain the area of various tree species. To evaluate the accuracy of our methodology, we compared the canopy boundary of the original image to the four experimental areas that were vectorized by CSVTSM. Our model’s efficacy was assessed by calculating the overall accuracy and Kappa coefficient on the validation set. The effectiveness of the proposed method was qualitatively evaluated by calculating the difference between the tree species area extracted by CSVTSM and manual extraction. The results indicate that, when considering the same canopy size, the vectorization approach based on the SLIC-based clustering label generates vector boundaries that are more closely aligned with the distribution of actual canopy boundaries in the experimental area. Furthermore, when used in conjunction with the SLIC vectorization approach, the ConvNeXt model can produce an even more precise vector map of tree species and more accurate tree species area information is obtained (only a 0.26 ha difference from manual extraction). These findings demonstrate that CSVTSM can be used to quantitatively acquire a wealth of information on individual tree positions, crown distribution ranges, and planting areas from high-resolution RGB photos captured by low-consumption unmanned aerial vehicle platforms. The implications of these results are wide-ranging, with local managers expressing keen interest in our findings as they offer trustworthy support for statistical application fields, such as gathering area ecological information, locating tree species resources, and disseminating spatial information about forests..