Methods and systems for detecting emission sites include identifying a set of known emitters having visible features and a spectroscopic signature that correspond to sites that emit a substance to form a training set. A classifier is generated based on the training set. New emitters are identified based on the classifier, a spectroscopic signature map, and a map of visible features. An alert is provided responsive to the identification of a new emitter.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for detecting emission sites, comprising: identifying a set of known emitters having visible features and a spectroscopic signature that correspond to sites that emit a substance to form a training set; generating a classifier based on the training set; identifying new emitters based on the classifier, a spectroscopic signature map, and a map of visible features, using a processor; and providing an alert responsive to the identification of a new emitter. 2. The method of claim 1, wherein identifying the set of known emitters comprises identifying regions where the spectroscopic signature is significantly above an average spectroscopic signature. 3. The method of claim 1, wherein identifying new emitters comprises applying the classifier in regions indicated by the spectroscopic signature map as having a higher spectroscopic signature than average to locate sites that are likely emitters. 4. The method of claim 1, wherein the visible features include features associated with industrial or agricultural sites. 5. The method of claim 4, wherein the visible features comprise one or more of the group consisting of: an entry road, a generally rectilinear plot, storage tanks, a lack of vegetation, the presence of industrial equipment, bodies of water, fences, and a size or orientation of buildings. 6. The method of claim 1, wherein generating the classifier comprises learning the classifier by machine learning. 7. The method of claim 6, wherein machine learning comprises training an artificial neural network. 8. The method of claim 1, further comprising generating the map of visible features and the spectroscopic signature map by satellite. 9. The method of claim 1, wherein the substance is methane and the spectroscopic signature is an absorption signal at about 1.65 μm. 10. A non-transitory computer readable storage medium comprising a computer readable program for detecting emission sites, wherein the computer readable program when executed on a computer causes the computer to perform the steps of claim 1. 11. A method for detecting emission sites, comprising: identifying a set of known methane emitters having visible features and a spectroscopic signature that is significantly above an average spectroscopic signature of methane at about 1.65 μm to form a training set; generating a classifier based on the training set using machine learning; identifying new emitters based on the classifier, a spectroscopic signature map, and a map of visible features by applying the classifier in regions indicated by the spectroscopic signature map as having a higher than average spectroscopic signature of methane at about 1.65 μm, using a processor; and providing an alert responsive to the identification of a new emitter. 12. A system for detecting emission sites, comprising: a training module configured to identify a set of known emitters having visible features and a spectroscopic signature that correspond to sites that emit a substance to form a training set; a machine learning module comprising a processor configured to generate a classifier based on the training set and to identify new emitters based on the classifier, a spectroscopic signature map, and a map of visible features; and an alert module configured to provide an alert responsive to the identification of a new emitter. 13. The system of claim 12, wherein the training module is further configured to identify regions where the spectroscopic signature is significantly above an average spectroscopic signature. 14. The system of claim 12, wherein the machine learning module is further configured to apply the classifier in regions indicated by the spectroscopic signature map as having a higher spectroscopic signature than average to locate sites that are likely emitters. 15. The system of claim 12, wherein the visible features include features associated with industrial or agricultural sites. 16. The system of claim 15, wherein the visible features comprise one or more of the group consisting of: an entry road, a generally rectilinear plot, storage tanks, a lack of vegetation, the presence of industrial equipment, bodies of water, fences, and a size or orientation of buildings. 17. The system of claim 12, wherein the machine learning module is further configured to learn the classifier by machine learning. 18. The system of claim 17, wherein machine learning comprises training an artificial neural network. 19. The system of claim 12, further comprising a memory configured to store the map of visible features and the spectroscopic signature map as supplied by one or more satellites. 20. The system of claim 12, wherein the substance is methane and the spectroscopic signature is an absorption signal at about 1.65 μm.