The Indo-Malaysian region is a hot spot of rapid land-cover and land-use change (LCLUC) with little consensus about the rates and magnitudes of such change. Here we use temporal convolutional neural networks (TempCNNs) to generate a spatiotemporally consistent LCLUC data set for nearly thirty-five years (1982–2015), validated against two reference data sets with over 80 percent accuracy, better than other LCLUC products for the region. Our results both confirm and complicate estimates from earlier work that relied on decadal, rather than interannual, changes in regional land cover. We find forests decrease in mainland and maritime Southeast Asia and increase in South China and South Asia. Consistent with geographic theory about the drivers of land-use change, we find cropland expansion is a driving force for deforestation in mainland Southeast Asia with savannas playing a superior role, suggesting widespread forest degradation in this region. In contrast to earlier work and theory, we find that South China’s increasing forest cover comes principally from savanna (rather than cropland) conversion. The explicit interannual LCLUC patterns, rates, and transitions identified in this study provide a valuable data source for studies of land-use theory, environmental and climate changes, and regional land-use policy evaluations.

印度-马来西亚地区是土地覆盖和土地利用快速变化（LCLUC）的热点地区，对于这种变化的速度和幅度几乎没有共识。在这里，我们使用时间卷积神经网络 (TempCNN) 生成近三十五年（1982-2015）的时空一致 LCLUC 数据集，针对两个参考数据集进行验证，准确率超过 80%，优于该地区的其他 LCLUC 产品. 我们的结果既证实了早期工作的估计，也使估计更加复杂，这些工作依赖于区域土地覆盖的十年而不是年际变化。我们发现东南亚大陆和沿海地区的森林减少，而华南和南亚的森林增加。与关于土地利用变化驱动因素的地理学理论一致，我们发现农田扩张是东南亚大陆森林砍伐的驱动力，其中稀树草原发挥了重要作用，这表明该地区森林退化普遍存在。与早期的工作和理论相比，我们发现华南地区增加的森林覆盖主要来自热带稀树草原（而不是农田）的转变。本研究确定的明确的年际 LCLUC 模式、速率和转变为土地利用理论、环境和气候变化以及区域土地利用政策评估的研究提供了宝贵的数据源。