As climate warms up and ice melts, the Arctic is drawing much more attention. It is undeniable that Arctic urban spatial information is critical for studying, understanding, and exploring the Arctic. Due to the special geographical situation, Arctic urban extraction has unique difficulties such as urban fragmentation and confusion with bare mountains. To overcome the problems of extracting Arctic urban, multi-source data including Landsat, DMSP/OLS, and ASTER-GDEM2 were used. Spectral features, texture features, nighttime light features, and topographic features were obtained after feature extraction. Apart from that, the AdaBoost algorithm was used to extract the urban areas at 1990, 2004 and 2016. To clearly and more completely understand the function of each feature, we divided features into four different groups, and compared their differences. The result shows that, adding terrain features or nighttime lighting features can improve the extraction accuracy, and that the combination of spectrum, texture, terrain, and nighttime lighting is the optimal combination of features. The overall accuracy (OA) and kappa values based on spectral and texture features are 86.20% and 0.68, respectively. After adding terrain feature, the accuracy increased by 2.7% (OA) and 6.21% (kappa) respectively. When only adding nighttime lighting feature, OA increased by 2.1% and kappa 0.50. The best result was reached when we added terrain feature and nighttime lighting simultaneously. In this case, the overall accuracy and kappa increased by 3.7% and 8.55%, respectively. So, it is the optimal combination of features. After identifying the optimal feature combination, the maximum likelihood method was used to extract urban areas to prove the effectiveness of the AdaBoost algorithm. Experiment results show that, with the optimal feature combination, extraction based on AdaBoost has its OA and kappa value 10% and 20% higher respectively than those by the maximum likelihood method. Finally, the urban expansion was analyzed. The intensity of the urban expansion in the study area is around 4.4*10~(-3) from 1990 to 2004 and this number is 4.5*10~(-3) from 2004 to 2016, which can be interpreted as slow expansion. The average level of expansion is 0.018, 1/2 of the global average. The urban expansion level between 1990 and 2004 is higher than that between 2004 and 2016. The difference in the dynamics during 1990-2004 and during 2004-2016 indicates that the study area is currently transitioning from a high-speed development period to a stable development period. Given the warming of the Arctic and the growing of population, Arctic urban is expected to continue expanding slowly.