To　realize　energy　conservation　and　environmental　protection,　solar　street　lights　have　been　widely　used　in　urban　areas　in　China.　To　reasonably　and　effectively　utilize　solar　street　lights,　the　original　street　lights　must　be　located,　and　the　solar　street　light　potential　must　be　assessed.　The　Jilin1-03B　(JL1-3B)　satellite　provides　next-generation　nighttime　light　data　with　a　high　spatial　resolution　and　in　three　spectral　bands.　Consequently,　the　street　lights　can　be　extracted　from　the　nighttime　light　data.　We　used　the　road　network　dataset　from　the　open　street　map　with　a　specific　buffer　to　extract　the　road　area　as　a　constraint　region.　Next,　the　grayscale　brightness　of　JL1-3B　images　was　obtained　by　integrating　all　the　three　bands　to　locate　the　street　light　by　using　a　local　maximum　algorithm.　Then,　the　values　of　the　original　three　bands　were　utilized　to　classify　the　types　of　street　lights　as　high-pressure　sodium　(HPS)　lamps　or　light-emitting　diode　lamps.　Finally,　we　simulated　the　replacement　of　all　the　HPS　lamps　with　solar　street　lights　and　assessed　the　corresponding　solar　energy　potential　by　using　the　digital　surface　model　data　and　hourly　cloud　cover　data　through　the　SHORTWAVE-C　model.　The　accuracy　of　location　of　the　street　lights　was　approximately　90%.　Replacing　an　HPS　lamp　by　one　solar　street　light　for　20　years　can　save　1.85　x　10(4)　kWh　of　electrical　energy,　7.41　t　of　standard　coal,　5.03　t　of　C　emissions,　18.47　t　of　CO2　emissions,　0.55　t　of　SO2　emissions,　and　0.28　t　of　NOX　emissions.