Wildlife-vehicle collision - WVC is a phenomenon that arises from the fragmentation of ecosystems by roads, limiting the mobility of individuals and putting at risk the stability of populations by increasing mortality. Colombia is not unaware of the problem of the WVC, evidenced in different scientific publications that describe the WVC in the roads of the country. Although the rise of artificial intelligence has significant advances in the prediction of spatial phenomena in recent years, it has not yet been sufficiently explored by Road Ecology. For this reason, this research aimed to develop a methodology to predict the sites of accumulation of WVC in eastern Antioquia, Colombia, based on artificial intelligence algorithms, geographic information systems - GIS, and multispectral image processing. During the development of this research, it was identified that the features most related to the WVC in the study area are: Distance to Forest, Distance to Biological Corridor, Ground Resistance to Movement, Cost of Movement, the bands of the Landsat 8 satellite: 9, 10, 11 and the normalized burning index (NBRI). Different machine learning algorithms were compared (k-nearest neighbours, support vector machines (SVM), random forests (RF), and artificial neural networks). SMOTE and ADASYN balancing techniques were applied. The results allowed to identify that the RF algorithm with ADASYN yielded the best performance when subjected to spatial-wise cross-validation (AUC-ROC 0.78 ± 0.12), surpassing the results of current state-of-the-art. Finally, the methodology was validated through a transfer learning experiment, training the RF-ADASYN algorithm with three zones of the eastern Antioquia region and validating on a different section (AUC-ROC = 0.87 ± 0.09), retraining the initial model with 5% of data from the validation database.

野生动物与车辆的碰撞-WVC是由于道路破坏生态系统而造成的现象，限制了个人的流动性，并通过增加死亡率而使种群的稳定性处于危险之中。哥伦比亚并没有意识到WVC的问题，在描述该国道路上的WVC的不同科学出版物中都证明了这一点。尽管近年来人工智能的兴起在空间现象的预测方面取得了重大进展，但道路生态学尚未对其进行充分的探索。因此，本研究旨在基于人工智能算法，地理信息系统-GIS和多光谱图像处理，开发一种方法来预测哥伦比亚东部Antioquia的WVC堆积位置。在这项研究的发展过程中，研究区域内与WVC最相关的特征是：距森林的距离，距生物走廊的距离，地面对运动的抵抗力，运动成本，Landsat 8卫星的波段：9、10、11和归一化燃烧指数（NBRI）。比较了不同的机器学习算法（k近邻，支持向量机（SVM），随机森林（RF）和人工神经网络）。应用了SMOTE和ADASYN平衡技术。结果表明，采用ADASYN的RF算法在进行空间交叉验证（AUC-ROC 0.78±0.12）时，表现出最佳性能，超过了当前的最新结果。最后，通过转移学习实验验证了该方法，